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For example, a resnet50", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 141, + 354, + 469, + 366 + ], + "spans": [ + { + "bbox": [ + 141, + 354, + 398, + 366 + ], + "score": 1.0, + "content": "trained on Imagenet sees its “barn spider” test accuracy falls from", + "type": "text" + }, + { + "bbox": [ + 399, + 354, + 419, + 365 + ], + "score": 0.88, + "content": "6 8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 419, + 354, + 429, + 366 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 429, + 354, + 449, + 365 + ], + "score": 0.88, + "content": "4 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 449, + 354, + 469, + 366 + ], + "score": 1.0, + "content": "only", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 142, + 365, + 469, + 377 + ], + "spans": [ + { + "bbox": [ + 142, + 365, + 469, + 377 + ], + "score": 1.0, + "content": "by introducing random crop DA during training. Even more surprising, such unfair", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 141, + 376, + 470, + 389 + ], + "spans": [ + { + "bbox": [ + 141, + 376, + 470, + 389 + ], + "score": 1.0, + "content": "impact of regularization also appears when introducing uninformative regularizers", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 141, + 387, + 470, + 399 + ], + "spans": [ + { + "bbox": [ + 141, + 387, + 470, + 399 + ], + "score": 1.0, + "content": "such as weight decay or dropout. Those results demonstrate that our search for ever", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 141, + 398, + 470, + 411 + ], + "spans": [ + { + "bbox": [ + 141, + 398, + 470, + 411 + ], + "score": 1.0, + "content": "increasing generalization performance —averaged over all classes and samples—", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 141, + 408, + 469, + 421 + ], + "spans": [ + { + "bbox": [ + 141, + 408, + 469, + 421 + ], + "score": 1.0, + "content": "has left us with models and regularizers that silently sacrifice performances on", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 141, + 420, + 469, + 432 + ], + "spans": [ + { + "bbox": [ + 141, + 420, + 469, + 432 + ], + "score": 1.0, + "content": "some classes. This scenario can become dangerous when deploying a model on", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 141, + 431, + 470, + 442 + ], + "spans": [ + { + "bbox": [ + 141, + 431, + 470, + 442 + ], + "score": 1.0, + "content": "downstream tasks e.g. an Imagenet pre-trained resnet50 deployed on INaturalist", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 141, + 441, + 470, + 453 + ], + "spans": [ + { + "bbox": [ + 141, + 441, + 267, + 453 + ], + "score": 1.0, + "content": "sees its performances fall from", + "type": "text" + }, + { + "bbox": [ + 267, + 441, + 287, + 452 + ], + "score": 0.89, + "content": "7 0 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 287, + 441, + 298, + 453 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 298, + 441, + 318, + 452 + ], + "score": 0.89, + "content": "3 0 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 318, + 441, + 352, + 453 + ], + "score": 1.0, + "content": "on class", + "type": "text" + }, + { + "bbox": [ + 353, + 441, + 379, + 452 + ], + "score": 0.51, + "content": "\\# 8 8 8 9", + "type": "inline_equation" + }, + { + "bbox": [ + 379, + 441, + 470, + 453 + ], + "score": 1.0, + "content": "when introducing ran-", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 141, + 452, + 470, + 465 + ], + "spans": [ + { + "bbox": [ + 141, + 452, + 470, + 465 + ], + "score": 1.0, + "content": "dom crop DA during the Imagenet pre-training phase. Those results demonstrate", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 141, + 464, + 469, + 475 + ], + "spans": [ + { + "bbox": [ + 141, + 464, + 469, + 475 + ], + "score": 1.0, + "content": "that finding a correct measure of a model’s complexity without class-dependent", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 141, + 475, + 329, + 487 + ], + "spans": [ + { + "bbox": [ + 141, + 475, + 329, + 487 + ], + "score": 1.0, + "content": "preference remains an open research question.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 24, + "bbox_fs": [ + 141, + 278, + 471, + 487 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 507, + 190, + 520 + ], + "lines": [ + { + "bbox": [ + 105, + 505, + 192, + 522 + ], + "spans": [ + { + "bbox": [ + 105, + 505, + 192, + 522 + ], + "score": 1.0, + "content": "1 Introduction", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 34 + }, + { + "type": "text", + "bbox": [ + 107, + 532, + 505, + 663 + ], + "lines": [ + { + "bbox": [ + 105, + 532, + 506, + 546 + ], + "spans": [ + { + "bbox": [ + 105, + 532, + 506, + 546 + ], + "score": 1.0, + "content": "Machine learning and deep learning aim at learning systems to solve as accurately as possible a", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 543, + 507, + 556 + ], + "spans": [ + { + "bbox": [ + 105, + 543, + 507, + 556 + ], + "score": 1.0, + "content": "given task at hand [LeCun et al., 1998, Bishop and Nasrabadi, 2006, Jordan and Mitchell, 2015].", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 106, + 555, + 506, + 567 + ], + "spans": [ + { + "bbox": [ + 106, + 555, + 506, + 567 + ], + "score": 1.0, + "content": "This process often takes the form of (i) being given a finite dataset, a (differentiable) loss function,", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 564, + 506, + 579 + ], + "spans": [ + { + "bbox": [ + 105, + 564, + 506, + 579 + ], + "score": 1.0, + "content": "and a performance measure, (ii) splitting the dataset into train/valid/test sets to optimizing the", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 576, + 506, + 589 + ], + "spans": [ + { + "bbox": [ + 105, + 576, + 506, + 589 + ], + "score": 1.0, + "content": "system’s parameters e.g. from gradient updates of the loss on the train set while cross-validating", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 587, + 506, + 599 + ], + "spans": [ + { + "bbox": [ + 105, + 587, + 506, + 599 + ], + "score": 1.0, + "content": "hyper-parameters using the valid set, and (iii) assessing the system’s performance on the test set. As", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 597, + 505, + 611 + ], + "spans": [ + { + "bbox": [ + 105, + 597, + 505, + 611 + ], + "score": 1.0, + "content": "the training set is finite, and the optimal design of the system is unknown, it is common to employ", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 609, + 505, + 621 + ], + "spans": [ + { + "bbox": [ + 105, + 609, + 505, + 621 + ], + "score": 1.0, + "content": "regularization during the optimization phase to reduce over-fitting [Tikhonov, 1943, Tihonov, 1963]", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 620, + 506, + 632 + ], + "spans": [ + { + "bbox": [ + 105, + 620, + 506, + 632 + ], + "score": 1.0, + "content": "i.e. to decrease the system’s performance gap between train set and test set samples [Simard et al.,", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 630, + 506, + 643 + ], + "spans": [ + { + "bbox": [ + 105, + 630, + 506, + 643 + ], + "score": 1.0, + "content": "1991, Chapelle et al., 2000, Bottou, 2012, Neyshabur et al., 2014]. Central to our study is the fact that", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 642, + 505, + 654 + ], + "spans": [ + { + "bbox": [ + 105, + 642, + 505, + 654 + ], + "score": 1.0, + "content": "hyper-parameter selection is done via cross-validation by maximizing the valid set performance with", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 653, + 477, + 665 + ], + "spans": [ + { + "bbox": [ + 106, + 653, + 477, + 665 + ], + "score": 1.0, + "content": "ad-hoc statistics e.g. the average accuracy over all samples for classes in classification tasks.", + "type": "text" + } + ], + "index": 46 + } + ], + "index": 40.5, + "bbox_fs": [ + 105, + 532, + 507, + 665 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 669, + 506, + 713 + ], + "lines": [ + { + "bbox": [ + 105, + 668, + 507, + 681 + ], + "spans": [ + { + "bbox": [ + 105, + 668, + 507, + 681 + ], + "score": 1.0, + "content": "Cross-validation commonly involves many different types of regularization along with their “strengths”", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 106, + 680, + 506, + 691 + ], + "spans": [ + { + "bbox": [ + 106, + 680, + 506, + 691 + ], + "score": 1.0, + "content": "[Goodfellow et al., 2016, He et al., 2021]. Most variants of regularization take one of two forms:", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 104, + 689, + 506, + 705 + ], + "spans": [ + { + "bbox": [ + 104, + 689, + 506, + 705 + ], + "score": 1.0, + "content": "Data-Augmentation (DA) and weight-decay. DA is a data-driven and informed regularization strategy", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 106, + 702, + 505, + 714 + ], + "spans": [ + { + "bbox": [ + 106, + 702, + 505, + 714 + ], + "score": 1.0, + "content": "that artificially increase the number of training samples [Shorten and Khoshgoftaar, 2019]. As", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 105, + 334, + 506, + 347 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 506, + 347 + ], + "score": 1.0, + "content": "opposed to most explicit regularizers e.g. Tikhonov regularization [Krogh and Hertz, 1991], also", + "type": "text", + "cross_page": true + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 345, + 506, + 357 + ], + "spans": [ + { + "bbox": [ + 106, + 345, + 506, + 357 + ], + "score": 1.0, + "content": "denoted as weight decay, DA’s regularization is implicit as it is not a function of a model’s parameter,", + "type": "text", + "cross_page": true + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 356, + 506, + 368 + ], + "spans": [ + { + "bbox": [ + 106, + 356, + 506, + 368 + ], + "score": 1.0, + "content": "but a function of the training samples [Neyshabur et al., 2014, Hernández-García and König, 2018,", + "type": "text", + "cross_page": true + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 366, + 506, + 379 + ], + "spans": [ + { + "bbox": [ + 105, + 366, + 506, + 379 + ], + "score": 1.0, + "content": "LeJeune et al., 2019]; although some DA strategies can be turned into explicit regularizers Balestriero", + "type": "text", + "cross_page": true + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 378, + 505, + 390 + ], + "spans": [ + { + "bbox": [ + 105, + 378, + 505, + 390 + ], + "score": 1.0, + "content": "et al. [2022]. Nevertheless, a key distinction between DA and weight decay is that DA tends to", + "type": "text", + "cross_page": true + } + ], + "index": 16 + }, + { + "bbox": [ + 104, + 388, + 506, + 401 + ], + "spans": [ + { + "bbox": [ + 104, + 388, + 506, + 401 + ], + "score": 1.0, + "content": "require more domain knowledge to be successful than weight decay. Most —if not all— of current", + "type": "text", + "cross_page": true + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 400, + 505, + 412 + ], + "spans": [ + { + "bbox": [ + 106, + 400, + 505, + 412 + ], + "score": 1.0, + "content": "state-of-the-art employ such regularizers [Huang et al., 2018, Chen et al., 2020b, Liu et al., 2021, Tan", + "type": "text", + "cross_page": true + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 411, + 232, + 423 + ], + "spans": [ + { + "bbox": [ + 106, + 411, + 232, + 423 + ], + "score": 1.0, + "content": "and Le, 2021, Liu et al., 2022].", + "type": "text", + "cross_page": true + } + ], + "index": 19 + } + ], + "index": 48.5, + "bbox_fs": [ + 104, + 668, + 507, + 714 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 118, + 77, + 498, + 215 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 118, + 77, + 498, + 215 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 118, + 77, + 498, + 215 + ], + "spans": [ + { + "bbox": [ + 118, + 77, + 498, + 215 + ], + "score": 0.92, + "type": "image", + "image_path": "0a582fb6f48f5b79c70845ea818ec095486cbfc5c65229ebf025a2f650a7a805.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 118, + 77, + 498, + 123.0 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 118, + 123.0, + 498, + 169.0 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 118, + 169.0, + 498, + 215.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 223, + 505, + 314 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 223, + 506, + 236 + ], + "spans": [ + { + "bbox": [ + 105, + 223, + 506, + 236 + ], + "score": 1.0, + "content": "Figure 1: Structural risk minimization minimizes the empirical risk of several models of varying complexity, and", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 235, + 506, + 245 + ], + "spans": [ + { + "bbox": [ + 106, + 235, + 506, + 245 + ], + "score": 1.0, + "content": "selects the one offering the best compromise between under-fitting and over-fitting [Vapnik and Chervonenkis,", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 244, + 506, + 256 + ], + "spans": [ + { + "bbox": [ + 105, + 244, + 506, + 256 + ], + "score": 1.0, + "content": "1974]. In deep learning, one commonly control the model’s complexity by picking different DN architectures", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 253, + 506, + 265 + ], + "spans": [ + { + "bbox": [ + 105, + 253, + 506, + 265 + ], + "score": 1.0, + "content": "and/or by applying different levels and flavors of regularization. The key observation of our study is that when the", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 264, + 505, + 275 + ], + "spans": [ + { + "bbox": [ + 106, + 264, + 505, + 275 + ], + "score": 1.0, + "content": "model complexity is calibrated by DA (see Figs. 2, 5 and 6), or weight-decay (see Fig. 3), the class-conditional", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 273, + 506, + 285 + ], + "spans": [ + { + "bbox": [ + 105, + 273, + 506, + 285 + ], + "score": 1.0, + "content": "empirical risks do not align between classes i.e. cross-validation produces models that perform well on the", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 283, + 506, + 296 + ], + "spans": [ + { + "bbox": [ + 105, + 283, + 506, + 296 + ], + "score": 1.0, + "content": "majority of classes but arbitrarily poorly on a few of them as depicted on the left-hand-side. In an ideal setting", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 293, + 505, + 305 + ], + "spans": [ + { + "bbox": [ + 105, + 293, + 505, + 305 + ], + "score": 1.0, + "content": "where the control of the model’s complexity is well aligned with the task and model, one would observe the", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 304, + 432, + 315 + ], + "spans": [ + { + "bbox": [ + 106, + 304, + 432, + 315 + ], + "score": 1.0, + "content": "right-hand-side ideal scenario where the same model complexity is optimal for all classes.", + "type": "text" + } + ], + "index": 11 + } + ], + "index": 7 + } + ], + "index": 4.0 + }, + { + "type": "text", + "bbox": [ + 107, + 334, + 506, + 422 + ], + "lines": [ + { + "bbox": [ + 105, + 334, + 506, + 347 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 506, + 347 + ], + "score": 1.0, + "content": "opposed to most explicit regularizers e.g. Tikhonov regularization [Krogh and Hertz, 1991], also", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 345, + 506, + 357 + ], + "spans": [ + { + "bbox": [ + 106, + 345, + 506, + 357 + ], + "score": 1.0, + "content": "denoted as weight decay, DA’s regularization is implicit as it is not a function of a model’s parameter,", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 356, + 506, + 368 + ], + "spans": [ + { + "bbox": [ + 106, + 356, + 506, + 368 + ], + "score": 1.0, + "content": "but a function of the training samples [Neyshabur et al., 2014, Hernández-García and König, 2018,", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 366, + 506, + 379 + ], + "spans": [ + { + "bbox": [ + 105, + 366, + 506, + 379 + ], + "score": 1.0, + "content": "LeJeune et al., 2019]; although some DA strategies can be turned into explicit regularizers Balestriero", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 378, + 505, + 390 + ], + "spans": [ + { + "bbox": [ + 105, + 378, + 505, + 390 + ], + "score": 1.0, + "content": "et al. [2022]. Nevertheless, a key distinction between DA and weight decay is that DA tends to", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 104, + 388, + 506, + 401 + ], + "spans": [ + { + "bbox": [ + 104, + 388, + 506, + 401 + ], + "score": 1.0, + "content": "require more domain knowledge to be successful than weight decay. Most —if not all— of current", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 400, + 505, + 412 + ], + "spans": [ + { + "bbox": [ + 106, + 400, + 505, + 412 + ], + "score": 1.0, + "content": "state-of-the-art employ such regularizers [Huang et al., 2018, Chen et al., 2020b, Liu et al., 2021, Tan", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 411, + 232, + 423 + ], + "spans": [ + { + "bbox": [ + 106, + 411, + 232, + 423 + ], + "score": 1.0, + "content": "and Le, 2021, Liu et al., 2022].", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 15.5 + }, + { + "type": "text", + "bbox": [ + 107, + 426, + 505, + 558 + ], + "lines": [ + { + "bbox": [ + 106, + 427, + 505, + 439 + ], + "spans": [ + { + "bbox": [ + 106, + 427, + 505, + 439 + ], + "score": 1.0, + "content": "In this paper, we will demonstrate that when cross-validation is employed to select the regularization", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 439, + 506, + 450 + ], + "spans": [ + { + "bbox": [ + 105, + 439, + 506, + 450 + ], + "score": 1.0, + "content": "settings maximizing the validation performance, a significant bias is introduced into the trained model:", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 448, + 506, + 462 + ], + "spans": [ + { + "bbox": [ + 105, + 448, + 506, + 462 + ], + "score": 1.0, + "content": "the regularized model exhibits strong per-class favoritism i.e. while the average test performance", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 460, + 506, + 473 + ], + "spans": [ + { + "bbox": [ + 105, + 460, + 506, + 473 + ], + "score": 1.0, + "content": "is improved, it is at the cost of producing a model with significant performance drop on some of", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 471, + 506, + 483 + ], + "spans": [ + { + "bbox": [ + 106, + 471, + 506, + 483 + ], + "score": 1.0, + "content": "the classes as illustrated in the schematic of Fig. 1. For readers familiar with statistical estimation", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 482, + 506, + 494 + ], + "spans": [ + { + "bbox": [ + 106, + 482, + 506, + 494 + ], + "score": 1.0, + "content": "results e.g. the bias-variance trade-off [Kohavi et al., 1996, Von Luxburg and Schölkopf, 2011] or", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 493, + 505, + 505 + ], + "spans": [ + { + "bbox": [ + 105, + 493, + 505, + 505 + ], + "score": 1.0, + "content": "bayesian estimation e.g. Tikhonov regularization [Box and Tiao, 2011, Gruber, 2017], it should not be", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 503, + 506, + 516 + ], + "spans": [ + { + "bbox": [ + 105, + 503, + 506, + 516 + ], + "score": 1.0, + "content": "surprising that regularization produces bias (more details and background provided in Appendix A).", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 513, + 506, + 527 + ], + "spans": [ + { + "bbox": [ + 105, + 513, + 506, + 527 + ], + "score": 1.0, + "content": "In fact, it is beneficial to introduce bias through regularization if it results in a significant reduction of", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 525, + 505, + 537 + ], + "spans": [ + { + "bbox": [ + 106, + 525, + 505, + 537 + ], + "score": 1.0, + "content": "the estimator variance —when one minimizes the average empirical risk. However, the potentially", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 535, + 506, + 549 + ], + "spans": [ + { + "bbox": [ + 105, + 535, + 506, + 549 + ], + "score": 1.0, + "content": "dangerous effect of regularization that this study brings forward is that the bias introduced by", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 547, + 391, + 559 + ], + "spans": [ + { + "bbox": [ + 105, + 547, + 391, + 559 + ], + "score": 1.0, + "content": "regularization is class-dependent, including on transfer learning tasks.", + "type": "text" + } + ], + "index": 31 + } + ], + "index": 25.5 + }, + { + "type": "text", + "bbox": [ + 107, + 563, + 505, + 662 + ], + "lines": [ + { + "bbox": [ + 105, + 563, + 506, + 576 + ], + "spans": [ + { + "bbox": [ + 105, + 563, + 506, + 576 + ], + "score": 1.0, + "content": "To thoroughly validate this observation, we propose a variety of controlled experiments in Section 2.", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 573, + 506, + 587 + ], + "spans": [ + { + "bbox": [ + 105, + 573, + 506, + 587 + ], + "score": 1.0, + "content": "First, we carefully quantify the impact of DA, weight decay and dropout on the per-class performance", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 584, + 506, + 598 + ], + "spans": [ + { + "bbox": [ + 105, + 584, + 506, + 598 + ], + "score": 1.0, + "content": "of a model in Section 2.1, demonstrating that current deep learning finds itself in the scenario depicted", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 596, + 505, + 609 + ], + "spans": [ + { + "bbox": [ + 105, + 596, + 505, + 609 + ], + "score": 1.0, + "content": "on the left of Fig. 1. Then, we consider the task of transfer learning in Section 2.2 where it is again", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 607, + 506, + 621 + ], + "spans": [ + { + "bbox": [ + 105, + 607, + 506, + 621 + ], + "score": 1.0, + "content": "possible to identify again a per-class bias on the target dataset even though the regularization was", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 618, + 506, + 631 + ], + "spans": [ + { + "bbox": [ + 105, + 618, + 506, + 631 + ], + "score": 1.0, + "content": "applied on a different (source) training set. 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For readers familiar with statistical estimation", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 482, + 506, + 494 + ], + "spans": [ + { + "bbox": [ + 106, + 482, + 506, + 494 + ], + "score": 1.0, + "content": "results e.g. the bias-variance trade-off [Kohavi et al., 1996, Von Luxburg and Schölkopf, 2011] or", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 493, + 505, + 505 + ], + "spans": [ + { + "bbox": [ + 105, + 493, + 505, + 505 + ], + "score": 1.0, + "content": "bayesian estimation e.g. Tikhonov regularization [Box and Tiao, 2011, Gruber, 2017], it should not be", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 503, + 506, + 516 + ], + "spans": [ + { + "bbox": [ + 105, + 503, + 506, + 516 + ], + "score": 1.0, + "content": "surprising that regularization produces bias (more details and background provided in Appendix A).", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 513, + 506, + 527 + ], + "spans": [ + { + "bbox": [ + 105, + 513, + 506, + 527 + ], + "score": 1.0, + "content": "In fact, it is beneficial to introduce bias through regularization if it results in a significant reduction of", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 525, + 505, + 537 + ], + "spans": [ + { + "bbox": [ + 106, + 525, + 505, + 537 + ], + "score": 1.0, + "content": "the estimator variance —when one minimizes the average empirical risk. However, the potentially", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 535, + 506, + 549 + ], + "spans": [ + { + "bbox": [ + 105, + 535, + 506, + 549 + ], + "score": 1.0, + "content": "dangerous effect of regularization that this study brings forward is that the bias introduced by", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 547, + 391, + 559 + ], + "spans": [ + { + "bbox": [ + 105, + 547, + 391, + 559 + ], + "score": 1.0, + "content": "regularization is class-dependent, including on transfer learning tasks.", + "type": "text" + } + ], + "index": 31 + } + ], + "index": 25.5, + "bbox_fs": [ + 105, + 427, + 506, + 559 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 563, + 505, + 662 + ], + "lines": [ + { + "bbox": [ + 105, + 563, + 506, + 576 + ], + "spans": [ + { + "bbox": [ + 105, + 563, + 506, + 576 + ], + "score": 1.0, + "content": "To thoroughly validate this observation, we propose a variety of controlled experiments in Section 2.", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 573, + 506, + 587 + ], + "spans": [ + { + "bbox": [ + 105, + 573, + 506, + 587 + ], + "score": 1.0, + "content": "First, we carefully quantify the impact of DA, weight decay and dropout on the per-class performance", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 584, + 506, + 598 + ], + "spans": [ + { + "bbox": [ + 105, + 584, + 506, + 598 + ], + "score": 1.0, + "content": "of a model in Section 2.1, demonstrating that current deep learning finds itself in the scenario depicted", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 596, + 505, + 609 + ], + "spans": [ + { + "bbox": [ + 105, + 596, + 505, + 609 + ], + "score": 1.0, + "content": "on the left of Fig. 1. Then, we consider the task of transfer learning in Section 2.2 where it is again", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 607, + 506, + 621 + ], + "spans": [ + { + "bbox": [ + 105, + 607, + 506, + 621 + ], + "score": 1.0, + "content": "possible to identify again a per-class bias on the target dataset even though the regularization was", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 618, + 506, + 631 + ], + "spans": [ + { + "bbox": [ + 105, + 618, + 506, + 631 + ], + "score": 1.0, + "content": "applied on a different (source) training set. This latter scenario is particularly relevant in current times", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 629, + 506, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 629, + 506, + 642 + ], + "score": 1.0, + "content": "where it is common to deploy a large pre-trained model on a variety of tasks and raise an important", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 640, + 505, + 653 + ], + "spans": [ + { + "bbox": [ + 105, + 640, + 505, + 653 + ], + "score": 1.0, + "content": "issue: selecting the —on average— best performing pre-trained model can lead to catastrophic", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 106, + 651, + 383, + 663 + ], + "spans": [ + { + "bbox": [ + 106, + 651, + 383, + 663 + ], + "score": 1.0, + "content": "individual class performance even on for different downstream tasks.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 36, + "bbox_fs": [ + 105, + 563, + 506, + 663 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 667, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 667, + 505, + 680 + ], + "spans": [ + { + "bbox": [ + 106, + 667, + 505, + 680 + ], + "score": 1.0, + "content": "Our next Section 3 will aim at exploring possible explanations and solutions. First, we will provide", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 678, + 505, + 690 + ], + "spans": [ + { + "bbox": [ + 105, + 678, + 505, + 690 + ], + "score": 1.0, + "content": "a brief theoretical justification on why and when DA can be the cause of model bias (Section 3.1)", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 688, + 505, + 701 + ], + "spans": [ + { + "bbox": [ + 105, + 688, + 505, + 701 + ], + "score": 1.0, + "content": "regardless of the task and data at hand. This will shed light to a first possible issue: the DA parameters", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 697, + 505, + 714 + ], + "spans": [ + { + "bbox": [ + 105, + 697, + 505, + 714 + ], + "score": 1.0, + "content": "that make the transformed input preserve its label information vary depending on the class underlying", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 711, + 505, + 724 + ], + "spans": [ + { + "bbox": [ + 105, + 711, + 505, + 724 + ], + "score": 1.0, + "content": "statistics. In short, DA silently introduces class-imbalance in the training set. We propose a dedicated", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 259, + 505, + 271 + ], + "spans": [ + { + "bbox": [ + 106, + 259, + 505, + 271 + ], + "score": 1.0, + "content": "analysis of the label-preserving property of DA on different classes and models in Section 3.2. We", + "type": "text", + "cross_page": true + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 271, + 505, + 282 + ], + "spans": [ + { + "bbox": [ + 106, + 271, + 505, + 282 + ], + "score": 1.0, + "content": "then take on the task of searching for a possible solution by first reviewing known theoretical studies", + "type": "text", + "cross_page": true + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 280, + 506, + 294 + ], + "spans": [ + { + "bbox": [ + 105, + 280, + 506, + 294 + ], + "score": 1.0, + "content": "quantifying the interplay between regularization and bias in Section 3.3. Lastly, we propose some", + "type": "text", + "cross_page": true + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 291, + 505, + 304 + ], + "spans": [ + { + "bbox": [ + 105, + 291, + 505, + 304 + ], + "score": 1.0, + "content": "solutions of our own in Section 3.4 built from the gained insights of Section 3.2 using label-distillation", + "type": "text", + "cross_page": true + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 303, + 505, + 315 + ], + "spans": [ + { + "bbox": [ + 106, + 303, + 505, + 315 + ], + "score": 1.0, + "content": "and adaptive DA. All the codebase used to train the various models and to generate the figures is in the", + "type": "text", + "cross_page": true + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 314, + 180, + 326 + ], + "spans": [ + { + "bbox": [ + 105, + 314, + 180, + 326 + ], + "score": 1.0, + "content": "supplementary files.", + "type": "text", + "cross_page": true + } + ], + "index": 13 + } + ], + "index": 43, + "bbox_fs": [ + 105, + 667, + 505, + 724 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 107, + 70, + 502, + 190 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 107, + 70, + 502, + 190 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 107, + 70, + 502, + 190 + ], + "spans": [ + { + "bbox": [ + 107, + 70, + 502, + 190 + ], + "score": 0.968, + "type": "image", + "image_path": "cf86150e77df9c74677df4284187ee43dd9c86df8e08cd627401fed3e0886a04.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 107, + 70, + 502, + 110.0 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 107, + 110.0, + 502, + 150.0 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 107, + 150.0, + 502, + 190.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 191, + 505, + 241 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 189, + 506, + 203 + ], + "spans": [ + { + "bbox": [ + 105, + 189, + 304, + 203 + ], + "score": 1.0, + "content": "Figure 2: Varying the random crop DA lower bound", + "type": "text" + }, + { + "bbox": [ + 305, + 192, + 310, + 199 + ], + "score": 0.35, + "content": "\\mathbf { \\hat { x } }", + "type": "inline_equation" + }, + { + "bbox": [ + 311, + 189, + 352, + 203 + ], + "score": 1.0, + "content": "-axis) from", + "type": "text" + }, + { + "bbox": [ + 352, + 191, + 374, + 200 + ], + "score": 0.87, + "content": "100 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 375, + 189, + 384, + 203 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 385, + 191, + 398, + 200 + ], + "score": 0.85, + "content": "8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 398, + 189, + 506, + 203 + ], + "score": 1.0, + "content": "provides greater average test", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 199, + 505, + 212 + ], + "spans": [ + { + "bbox": [ + 105, + 199, + 505, + 212 + ], + "score": 1.0, + "content": "accuracy (blue) but makes the per-class performance fall for some of the classes. Images of each class are", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 210, + 505, + 222 + ], + "spans": [ + { + "bbox": [ + 105, + 210, + 505, + 222 + ], + "score": 1.0, + "content": "provided in Fig. 12, in the appendix. See Fig. 8 for the convnext and ViT experiments. Results obtained by", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 219, + 506, + 232 + ], + "spans": [ + { + "bbox": [ + 105, + 219, + 506, + 232 + ], + "score": 1.0, + "content": "averaging over 20 runs, official PyTorch resnet50 implementation trained on Imagenet with horizontal flip and", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 230, + 249, + 241 + ], + "spans": [ + { + "bbox": [ + 106, + 230, + 249, + 241 + ], + "score": 1.0, + "content": "varying random crop lower bound DA.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 5 + } + ], + "index": 3.0 + }, + { + "type": "text", + "bbox": [ + 107, + 259, + 505, + 325 + ], + "lines": [ + { + "bbox": [ + 106, + 259, + 505, + 271 + ], + "spans": [ + { + "bbox": [ + 106, + 259, + 505, + 271 + ], + "score": 1.0, + "content": "analysis of the label-preserving property of DA on different classes and models in Section 3.2. We", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 271, + 505, + 282 + ], + "spans": [ + { + "bbox": [ + 106, + 271, + 505, + 282 + ], + "score": 1.0, + "content": "then take on the task of searching for a possible solution by first reviewing known theoretical studies", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 280, + 506, + 294 + ], + "spans": [ + { + "bbox": [ + 105, + 280, + 506, + 294 + ], + "score": 1.0, + "content": "quantifying the interplay between regularization and bias in Section 3.3. Lastly, we propose some", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 291, + 505, + 304 + ], + "spans": [ + { + "bbox": [ + 105, + 291, + 505, + 304 + ], + "score": 1.0, + "content": "solutions of our own in Section 3.4 built from the gained insights of Section 3.2 using label-distillation", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 303, + 505, + 315 + ], + "spans": [ + { + "bbox": [ + 106, + 303, + 505, + 315 + ], + "score": 1.0, + "content": "and adaptive DA. All the codebase used to train the various models and to generate the figures is in the", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 314, + 180, + 326 + ], + "spans": [ + { + "bbox": [ + 105, + 314, + 180, + 326 + ], + "score": 1.0, + "content": "supplementary files.", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 10.5 + }, + { + "type": "title", + "bbox": [ + 107, + 336, + 501, + 364 + ], + "lines": [ + { + "bbox": [ + 106, + 336, + 501, + 353 + ], + "spans": [ + { + "bbox": [ + 106, + 336, + 501, + 353 + ], + "score": 1.0, + "content": "2 Maximizing the Average Model Performance by Cross-Validation Silently", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 123, + 351, + 446, + 366 + ], + "spans": [ + { + "bbox": [ + 123, + 351, + 446, + 366 + ], + "score": 1.0, + "content": "Produce Poor Final Performances on a Minority of the Classes", + "type": "text" + } + ], + "index": 15 + } + ], + "index": 14.5 + }, + { + "type": "text", + "bbox": [ + 107, + 370, + 505, + 447 + ], + "lines": [ + { + "bbox": [ + 106, + 370, + 506, + 384 + ], + "spans": [ + { + "bbox": [ + 106, + 370, + 506, + 384 + ], + "score": 1.0, + "content": "We now turn to the empirical validation of Fig. 1 i.e. quantifying the amount of class-dependent", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 381, + 506, + 394 + ], + "spans": [ + { + "bbox": [ + 105, + 381, + 506, + 394 + ], + "score": 1.0, + "content": "bias caused by DA, weight decay and dropout in various realistic scenarios (Section 2.1). We then", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "spans": [ + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "score": 1.0, + "content": "demonstrate how the bias introduced by regularization transfers to downstream tasks e.g. when", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 403, + 505, + 415 + ], + "spans": [ + { + "bbox": [ + 105, + 403, + 505, + 415 + ], + "score": 1.0, + "content": "deploying an Imagenet (source) trained model on the INaturalist (target) dataset in Section 2.2; that", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 414, + 504, + 426 + ], + "spans": [ + { + "bbox": [ + 106, + 414, + 504, + 426 + ], + "score": 1.0, + "content": "scenario is key as it demonstrates the potential harm of selecting the best performing model on the", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 425, + 505, + 437 + ], + "spans": [ + { + "bbox": [ + 105, + 425, + 505, + 437 + ], + "score": 1.0, + "content": "source dataset which could turn out to also be the most biased model against the target dataset class", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 435, + 152, + 448 + ], + "spans": [ + { + "bbox": [ + 105, + 435, + 152, + 448 + ], + "score": 1.0, + "content": "of interest.", + "type": "text" + } + ], + "index": 22 + } + ], + "index": 19 + }, + { + "type": "title", + "bbox": [ + 106, + 459, + 474, + 482 + ], + "lines": [ + { + "bbox": [ + 106, + 459, + 474, + 471 + ], + "spans": [ + { + "bbox": [ + 106, + 460, + 124, + 471 + ], + "score": 1.0, + "content": "2.1", + "type": "text" + }, + { + "bbox": [ + 127, + 459, + 474, + 471 + ], + "score": 1.0, + "content": "Precisely Measuring the Per-Class Effect of Data-Augmentation and Uninformed", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 128, + 471, + 321, + 483 + ], + "spans": [ + { + "bbox": [ + 128, + 471, + 321, + 483 + ], + "score": 1.0, + "content": "Regularization with Controlled Experiments", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 23.5 + }, + { + "type": "text", + "bbox": [ + 106, + 487, + 505, + 575 + ], + "lines": [ + { + "bbox": [ + 105, + 486, + 506, + 500 + ], + "spans": [ + { + "bbox": [ + 105, + 486, + 506, + 500 + ], + "score": 1.0, + "content": "This section aims at quantifying precisely the amount of downward or upward per-class performance", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 498, + 506, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 498, + 506, + 511 + ], + "score": 1.0, + "content": "shift that came as a result from using DA or uninformed regularization e.g. weight-decay or dropout.", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 508, + 506, + 521 + ], + "spans": [ + { + "bbox": [ + 105, + 508, + 506, + 521 + ], + "score": 1.0, + "content": "In fact, it is crucial to remember that regularization, or any other form of structural risk minimization,", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 520, + 506, + 532 + ], + "spans": [ + { + "bbox": [ + 105, + 520, + 506, + 532 + ], + "score": 1.0, + "content": "improves generalization performances by increasing the bias of the estimator so that the estimator’s", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 531, + 506, + 543 + ], + "spans": [ + { + "bbox": [ + 106, + 531, + 506, + 543 + ], + "score": 1.0, + "content": "variance is decreased by a greater amount. However, nothing guarantees the fairness of this bias i.e.", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 106, + 542, + 505, + 554 + ], + "spans": [ + { + "bbox": [ + 106, + 542, + 505, + 554 + ], + "score": 1.0, + "content": "for it to be equally distributed amongst the dataset classes. We thus propose a sensitivity analysis", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "spans": [ + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "score": 1.0, + "content": "by training a large collection of models with varying regularization policies to precisely assess their", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 564, + 278, + 576 + ], + "spans": [ + { + "bbox": [ + 106, + 564, + 278, + 576 + ], + "score": 1.0, + "content": "impact on the class-dependent model bias.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 28.5 + }, + { + "type": "text", + "bbox": [ + 107, + 579, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 579, + 504, + 591 + ], + "spans": [ + { + "bbox": [ + 106, + 579, + 504, + 591 + ], + "score": 1.0, + "content": "Data-Augmentation. DA samples have been known to sometimes disregard the semantic information", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 589, + 505, + 605 + ], + "spans": [ + { + "bbox": [ + 105, + 589, + 505, + 605 + ], + "score": 1.0, + "content": "of the original samples [Krizhevsky et al., 2012]. Nevertheless, DA remains applied universally", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 600, + 505, + 615 + ], + "spans": [ + { + "bbox": [ + 105, + 600, + 505, + 615 + ], + "score": 1.0, + "content": "across tasks and datasets [Shorten and Khoshgoftaar, 2019] as it provides significant performance", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 612, + 506, + 625 + ], + "spans": [ + { + "bbox": [ + 106, + 612, + 506, + 625 + ], + "score": 1.0, + "content": "improvements, even in semi-supervised and unsupervised settings Guo et al. [2018], Xie et al. [2020],", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 623, + 506, + 637 + ], + "spans": [ + { + "bbox": [ + 105, + 623, + 506, + 637 + ], + "score": 1.0, + "content": "Misra and Maaten [2020]. To measure the impact of DA onto per-class performances, we propose in", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 633, + 506, + 648 + ], + "spans": [ + { + "bbox": [ + 105, + 633, + 506, + 648 + ], + "score": 1.0, + "content": "Fig. 2 a sensitivity analysis by training the same architecture on Imagenet with varying DA policies.", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 645, + 505, + 659 + ], + "spans": [ + { + "bbox": [ + 105, + 645, + 505, + 659 + ], + "score": 1.0, + "content": "In particular, we consider a given DA (random crop in this case) and we vary the support of the", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 655, + 506, + 670 + ], + "spans": [ + { + "bbox": [ + 105, + 655, + 147, + 670 + ], + "score": 1.0, + "content": "parameter", + "type": "text" + }, + { + "bbox": [ + 148, + 658, + 156, + 666 + ], + "score": 0.76, + "content": "\\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 156, + 655, + 506, + 670 + ], + "score": 1.0, + "content": "which represents how much of the original image is kept in the crop (examples at the top", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 667, + 505, + 680 + ], + "spans": [ + { + "bbox": [ + 106, + 667, + 372, + 680 + ], + "score": 1.0, + "content": "of Fig. 5). We train multiple Deep Neural Networks (DNN)s using", + "type": "text" + }, + { + "bbox": [ + 372, + 667, + 422, + 679 + ], + "score": 0.93, + "content": "\\alpha \\in [ 1 0 0 , \\tau ]", + "type": "inline_equation" + }, + { + "bbox": [ + 422, + 667, + 443, + 680 + ], + "score": 1.0, + "content": "with", + "type": "text" + }, + { + "bbox": [ + 443, + 669, + 450, + 677 + ], + "score": 0.74, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 450, + 667, + 505, + 680 + ], + "score": 1.0, + "content": "varying from", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 104, + 676, + 506, + 691 + ], + "spans": [ + { + "bbox": [ + 104, + 676, + 506, + 691 + ], + "score": 1.0, + "content": "100 to 8 and for each case, we report our metrics averaged over 20 trained models. We observe a", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 689, + 505, + 701 + ], + "spans": [ + { + "bbox": [ + 106, + 689, + 505, + 701 + ], + "score": 1.0, + "content": "clear relation between increase in the strength of the DA, increase in the average test accuracy overall", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 698, + 507, + 714 + ], + "spans": [ + { + "bbox": [ + 105, + 698, + 507, + 714 + ], + "score": 1.0, + "content": "classes, and decrease in some per-class test accuracies. For example, on a resnet50 Imagenet setting,", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 711, + 471, + 724 + ], + "spans": [ + { + "bbox": [ + 106, + 711, + 324, + 724 + ], + "score": 1.0, + "content": "the accuracy on the “academic gown” class goes from", + "type": "text" + }, + { + "bbox": [ + 324, + 711, + 343, + 721 + ], + "score": 0.86, + "content": "62 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 344, + 711, + 354, + 724 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 355, + 711, + 374, + 721 + ], + "score": 0.87, + "content": "40 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 375, + 711, + 419, + 724 + ], + "score": 1.0, + "content": "steadily as", + "type": "text" + }, + { + "bbox": [ + 420, + 713, + 427, + 721 + ], + "score": 0.76, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 427, + 711, + 471, + 724 + ], + "score": 1.0, + "content": "decreases.", + "type": "text" + } + ], + "index": 45 + } + ], + "index": 39 + } + ], + "page_idx": 2, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 741, + 309, + 750 + ], + "lines": [ + { + "bbox": [ + 301, + 740, + 310, + 752 + ], + "spans": [ + { + "bbox": [ + 301, + 740, + 310, + 752 + ], + "score": 1.0, + "content": "3", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "image", + "bbox": [ + 107, + 70, + 502, + 190 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 107, + 70, + 502, + 190 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 107, + 70, + 502, + 190 + ], + "spans": [ + { + "bbox": [ + 107, + 70, + 502, + 190 + ], + "score": 0.968, + "type": "image", + "image_path": "cf86150e77df9c74677df4284187ee43dd9c86df8e08cd627401fed3e0886a04.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 107, + 70, + 502, + 110.0 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 107, + 110.0, + 502, + 150.0 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 107, + 150.0, + 502, + 190.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 191, + 505, + 241 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 189, + 506, + 203 + ], + "spans": [ + { + "bbox": [ + 105, + 189, + 304, + 203 + ], + "score": 1.0, + "content": "Figure 2: Varying the random crop DA lower bound", + "type": "text" + }, + { + "bbox": [ + 305, + 192, + 310, + 199 + ], + "score": 0.35, + "content": "\\mathbf { \\hat { x } }", + "type": "inline_equation" + }, + { + "bbox": [ + 311, + 189, + 352, + 203 + ], + "score": 1.0, + "content": "-axis) from", + "type": "text" + }, + { + "bbox": [ + 352, + 191, + 374, + 200 + ], + "score": 0.87, + "content": "100 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 375, + 189, + 384, + 203 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 385, + 191, + 398, + 200 + ], + "score": 0.85, + "content": "8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 398, + 189, + 506, + 203 + ], + "score": 1.0, + "content": "provides greater average test", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 199, + 505, + 212 + ], + "spans": [ + { + "bbox": [ + 105, + 199, + 505, + 212 + ], + "score": 1.0, + "content": "accuracy (blue) but makes the per-class performance fall for some of the classes. Images of each class are", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 210, + 505, + 222 + ], + "spans": [ + { + "bbox": [ + 105, + 210, + 505, + 222 + ], + "score": 1.0, + "content": "provided in Fig. 12, in the appendix. See Fig. 8 for the convnext and ViT experiments. Results obtained by", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 219, + 506, + 232 + ], + "spans": [ + { + "bbox": [ + 105, + 219, + 506, + 232 + ], + "score": 1.0, + "content": "averaging over 20 runs, official PyTorch resnet50 implementation trained on Imagenet with horizontal flip and", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 230, + 249, + 241 + ], + "spans": [ + { + "bbox": [ + 106, + 230, + 249, + 241 + ], + "score": 1.0, + "content": "varying random crop lower bound DA.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 5 + } + ], + "index": 3.0 + }, + { + "type": "text", + "bbox": [ + 107, + 259, + 505, + 325 + ], + "lines": [], + "index": 10.5, + "bbox_fs": [ + 105, + 259, + 506, + 326 + ], + "lines_deleted": true + }, + { + "type": "title", + "bbox": [ + 107, + 336, + 501, + 364 + ], + "lines": [ + { + "bbox": [ + 106, + 336, + 501, + 353 + ], + "spans": [ + { + "bbox": [ + 106, + 336, + 501, + 353 + ], + "score": 1.0, + "content": "2 Maximizing the Average Model Performance by Cross-Validation Silently", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 123, + 351, + 446, + 366 + ], + "spans": [ + { + "bbox": [ + 123, + 351, + 446, + 366 + ], + "score": 1.0, + "content": "Produce Poor Final Performances on a Minority of the Classes", + "type": "text" + } + ], + "index": 15 + } + ], + "index": 14.5 + }, + { + "type": "text", + "bbox": [ + 107, + 370, + 505, + 447 + ], + "lines": [ + { + "bbox": [ + 106, + 370, + 506, + 384 + ], + "spans": [ + { + "bbox": [ + 106, + 370, + 506, + 384 + ], + "score": 1.0, + "content": "We now turn to the empirical validation of Fig. 1 i.e. quantifying the amount of class-dependent", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 381, + 506, + 394 + ], + "spans": [ + { + "bbox": [ + 105, + 381, + 506, + 394 + ], + "score": 1.0, + "content": "bias caused by DA, weight decay and dropout in various realistic scenarios (Section 2.1). We then", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "spans": [ + { + "bbox": [ + 106, + 392, + 505, + 405 + ], + "score": 1.0, + "content": "demonstrate how the bias introduced by regularization transfers to downstream tasks e.g. when", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 403, + 505, + 415 + ], + "spans": [ + { + "bbox": [ + 105, + 403, + 505, + 415 + ], + "score": 1.0, + "content": "deploying an Imagenet (source) trained model on the INaturalist (target) dataset in Section 2.2; that", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 414, + 504, + 426 + ], + "spans": [ + { + "bbox": [ + 106, + 414, + 504, + 426 + ], + "score": 1.0, + "content": "scenario is key as it demonstrates the potential harm of selecting the best performing model on the", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 425, + 505, + 437 + ], + "spans": [ + { + "bbox": [ + 105, + 425, + 505, + 437 + ], + "score": 1.0, + "content": "source dataset which could turn out to also be the most biased model against the target dataset class", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 435, + 152, + 448 + ], + "spans": [ + { + "bbox": [ + 105, + 435, + 152, + 448 + ], + "score": 1.0, + "content": "of interest.", + "type": "text" + } + ], + "index": 22 + } + ], + "index": 19, + "bbox_fs": [ + 105, + 370, + 506, + 448 + ] + }, + { + "type": "title", + "bbox": [ + 106, + 459, + 474, + 482 + ], + "lines": [ + { + "bbox": [ + 106, + 459, + 474, + 471 + ], + "spans": [ + { + "bbox": [ + 106, + 460, + 124, + 471 + ], + "score": 1.0, + "content": "2.1", + "type": "text" + }, + { + "bbox": [ + 127, + 459, + 474, + 471 + ], + "score": 1.0, + "content": "Precisely Measuring the Per-Class Effect of Data-Augmentation and Uninformed", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 128, + 471, + 321, + 483 + ], + "spans": [ + { + "bbox": [ + 128, + 471, + 321, + 483 + ], + "score": 1.0, + "content": "Regularization with Controlled Experiments", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 23.5 + }, + { + "type": "text", + "bbox": [ + 106, + 487, + 505, + 575 + ], + "lines": [ + { + "bbox": [ + 105, + 486, + 506, + 500 + ], + "spans": [ + { + "bbox": [ + 105, + 486, + 506, + 500 + ], + "score": 1.0, + "content": "This section aims at quantifying precisely the amount of downward or upward per-class performance", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 498, + 506, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 498, + 506, + 511 + ], + "score": 1.0, + "content": "shift that came as a result from using DA or uninformed regularization e.g. weight-decay or dropout.", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 508, + 506, + 521 + ], + "spans": [ + { + "bbox": [ + 105, + 508, + 506, + 521 + ], + "score": 1.0, + "content": "In fact, it is crucial to remember that regularization, or any other form of structural risk minimization,", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 520, + 506, + 532 + ], + "spans": [ + { + "bbox": [ + 105, + 520, + 506, + 532 + ], + "score": 1.0, + "content": "improves generalization performances by increasing the bias of the estimator so that the estimator’s", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 531, + 506, + 543 + ], + "spans": [ + { + "bbox": [ + 106, + 531, + 506, + 543 + ], + "score": 1.0, + "content": "variance is decreased by a greater amount. However, nothing guarantees the fairness of this bias i.e.", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 106, + 542, + 505, + 554 + ], + "spans": [ + { + "bbox": [ + 106, + 542, + 505, + 554 + ], + "score": 1.0, + "content": "for it to be equally distributed amongst the dataset classes. We thus propose a sensitivity analysis", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "spans": [ + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "score": 1.0, + "content": "by training a large collection of models with varying regularization policies to precisely assess their", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 564, + 278, + 576 + ], + "spans": [ + { + "bbox": [ + 106, + 564, + 278, + 576 + ], + "score": 1.0, + "content": "impact on the class-dependent model bias.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 28.5, + "bbox_fs": [ + 105, + 486, + 506, + 576 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 579, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 579, + 504, + 591 + ], + "spans": [ + { + "bbox": [ + 106, + 579, + 504, + 591 + ], + "score": 1.0, + "content": "Data-Augmentation. DA samples have been known to sometimes disregard the semantic information", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 589, + 505, + 605 + ], + "spans": [ + { + "bbox": [ + 105, + 589, + 505, + 605 + ], + "score": 1.0, + "content": "of the original samples [Krizhevsky et al., 2012]. Nevertheless, DA remains applied universally", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 600, + 505, + 615 + ], + "spans": [ + { + "bbox": [ + 105, + 600, + 505, + 615 + ], + "score": 1.0, + "content": "across tasks and datasets [Shorten and Khoshgoftaar, 2019] as it provides significant performance", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 612, + 506, + 625 + ], + "spans": [ + { + "bbox": [ + 106, + 612, + 506, + 625 + ], + "score": 1.0, + "content": "improvements, even in semi-supervised and unsupervised settings Guo et al. [2018], Xie et al. [2020],", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 623, + 506, + 637 + ], + "spans": [ + { + "bbox": [ + 105, + 623, + 506, + 637 + ], + "score": 1.0, + "content": "Misra and Maaten [2020]. To measure the impact of DA onto per-class performances, we propose in", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 633, + 506, + 648 + ], + "spans": [ + { + "bbox": [ + 105, + 633, + 506, + 648 + ], + "score": 1.0, + "content": "Fig. 2 a sensitivity analysis by training the same architecture on Imagenet with varying DA policies.", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 645, + 505, + 659 + ], + "spans": [ + { + "bbox": [ + 105, + 645, + 505, + 659 + ], + "score": 1.0, + "content": "In particular, we consider a given DA (random crop in this case) and we vary the support of the", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 655, + 506, + 670 + ], + "spans": [ + { + "bbox": [ + 105, + 655, + 147, + 670 + ], + "score": 1.0, + "content": "parameter", + "type": "text" + }, + { + "bbox": [ + 148, + 658, + 156, + 666 + ], + "score": 0.76, + "content": "\\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 156, + 655, + 506, + 670 + ], + "score": 1.0, + "content": "which represents how much of the original image is kept in the crop (examples at the top", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 667, + 505, + 680 + ], + "spans": [ + { + "bbox": [ + 106, + 667, + 372, + 680 + ], + "score": 1.0, + "content": "of Fig. 5). We train multiple Deep Neural Networks (DNN)s using", + "type": "text" + }, + { + "bbox": [ + 372, + 667, + 422, + 679 + ], + "score": 0.93, + "content": "\\alpha \\in [ 1 0 0 , \\tau ]", + "type": "inline_equation" + }, + { + "bbox": [ + 422, + 667, + 443, + 680 + ], + "score": 1.0, + "content": "with", + "type": "text" + }, + { + "bbox": [ + 443, + 669, + 450, + 677 + ], + "score": 0.74, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 450, + 667, + 505, + 680 + ], + "score": 1.0, + "content": "varying from", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 104, + 676, + 506, + 691 + ], + "spans": [ + { + "bbox": [ + 104, + 676, + 506, + 691 + ], + "score": 1.0, + "content": "100 to 8 and for each case, we report our metrics averaged over 20 trained models. We observe a", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 689, + 505, + 701 + ], + "spans": [ + { + "bbox": [ + 106, + 689, + 505, + 701 + ], + "score": 1.0, + "content": "clear relation between increase in the strength of the DA, increase in the average test accuracy overall", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 698, + 507, + 714 + ], + "spans": [ + { + "bbox": [ + 105, + 698, + 507, + 714 + ], + "score": 1.0, + "content": "classes, and decrease in some per-class test accuracies. For example, on a resnet50 Imagenet setting,", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 711, + 471, + 724 + ], + "spans": [ + { + "bbox": [ + 106, + 711, + 324, + 724 + ], + "score": 1.0, + "content": "the accuracy on the “academic gown” class goes from", + "type": "text" + }, + { + "bbox": [ + 324, + 711, + 343, + 721 + ], + "score": 0.86, + "content": "62 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 344, + 711, + 354, + 724 + ], + "score": 1.0, + "content": "to", + "type": "text" + }, + { + "bbox": [ + 355, + 711, + 374, + 721 + ], + "score": 0.87, + "content": "40 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 375, + 711, + 419, + 724 + ], + "score": 1.0, + "content": "steadily as", + "type": "text" + }, + { + "bbox": [ + 420, + 713, + 427, + 721 + ], + "score": 0.76, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 427, + 711, + 471, + 724 + ], + "score": 1.0, + "content": "decreases.", + "type": "text" + } + ], + "index": 45 + } + ], + "index": 39, + "bbox_fs": [ + 104, + 579, + 507, + 724 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 107, + 70, + 501, + 190 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 107, + 70, + 501, + 190 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 107, + 70, + 501, + 190 + ], + "spans": [ + { + "bbox": [ + 107, + 70, + 501, + 190 + ], + "score": 0.971, + "type": "image", + "image_path": "a9f2b68ae848c586ddff72ac17bb6397ed8cefb20460c7f6deba497956f957c6.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 107, + 70, + 501, + 110.0 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 107, + 110.0, + 501, + 150.0 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 107, + 150.0, + 501, + 190.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 191, + 505, + 240 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 189, + 505, + 202 + ], + "spans": [ + { + "bbox": [ + 105, + 189, + 505, + 202 + ], + "score": 1.0, + "content": "Figure 3: Varying the amount of weight decay, an uninformed regularizer employed throughout, surprisingly", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 200, + 506, + 212 + ], + "spans": [ + { + "bbox": [ + 106, + 200, + 506, + 212 + ], + "score": 1.0, + "content": "exhibits similar class-dependent bias as the DA scenario of Fig. 2. Images for each class are provided in Fig. 13,", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 210, + 505, + 222 + ], + "spans": [ + { + "bbox": [ + 106, + 210, + 505, + 222 + ], + "score": 1.0, + "content": "in the appendix. Results obtained by averaging over 20 runs, official PyTorch resnet50 implementation trained", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 220, + 505, + 231 + ], + "spans": [ + { + "bbox": [ + 106, + 220, + 505, + 231 + ], + "score": 1.0, + "content": "on Imagenet with varying weight decay, see Fig. 16 for DenseNet121 results with the same trend and Fig. 8 for", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 230, + 235, + 243 + ], + "spans": [ + { + "bbox": [ + 106, + 230, + 235, + 243 + ], + "score": 1.0, + "content": "the convnext and ViT experiments.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 5 + } + ], + "index": 3.0 + }, + { + "type": "text", + "bbox": [ + 107, + 254, + 505, + 473 + ], + "lines": [ + { + "bbox": [ + 106, + 254, + 506, + 268 + ], + "spans": [ + { + "bbox": [ + 106, + 254, + 506, + 268 + ], + "score": 1.0, + "content": "Uninformed Regularization. As per the arguments given in Sections 3.1 and 3.2, it would be", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 266, + 506, + 278 + ], + "spans": [ + { + "bbox": [ + 105, + 266, + 506, + 278 + ], + "score": 1.0, + "content": "natural to assume that what makes DA responsible for creating class-dependent bias in DNNs is", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 275, + 506, + 290 + ], + "spans": [ + { + "bbox": [ + 105, + 275, + 506, + 290 + ], + "score": 1.0, + "content": "our misfortune in defining correct augmentation policies. Hence, uninformed weight decay or", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 286, + 505, + 300 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 505, + 300 + ], + "score": 1.0, + "content": "dropout should behave differently and more fairly. We demonstrate here that such regularizers are", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 297, + 506, + 312 + ], + "spans": [ + { + "bbox": [ + 105, + 297, + 506, + 312 + ], + "score": 1.0, + "content": "also unfair between classes. We thus propose to train multiple models with varying weight-decay", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 308, + 506, + 322 + ], + "spans": [ + { + "bbox": [ + 105, + 308, + 506, + 322 + ], + "score": 1.0, + "content": "and dropout parameters. In our setting, weight-decay is applied to all the DNN parameters except", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 320, + 506, + 333 + ], + "spans": [ + { + "bbox": [ + 106, + 320, + 506, + 333 + ], + "score": 1.0, + "content": "for the ones of batch-normalization layers, as commonly done [Hastie et al., 2009, Leclerc et al.,", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 329, + 506, + 345 + ], + "spans": [ + { + "bbox": [ + 105, + 329, + 506, + 345 + ], + "score": 1.0, + "content": "2022]. We report in Fig. 3 the per-class performance of a resnet50 trained on Imagenet with varying", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 342, + 505, + 354 + ], + "spans": [ + { + "bbox": [ + 106, + 342, + 205, + 354 + ], + "score": 1.0, + "content": "weight decay coefficient", + "type": "text" + }, + { + "bbox": [ + 205, + 343, + 213, + 353 + ], + "score": 0.82, + "content": "\\gamma", + "type": "inline_equation" + }, + { + "bbox": [ + 213, + 342, + 505, + 354 + ], + "score": 1.0, + "content": "(as was done for DA in Fig. 2) and we observe that different classes have", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 352, + 505, + 366 + ], + "spans": [ + { + "bbox": [ + 105, + 352, + 305, + 366 + ], + "score": 1.0, + "content": "different test accuracy sensitivities to variations in", + "type": "text" + }, + { + "bbox": [ + 306, + 354, + 313, + 364 + ], + "score": 0.79, + "content": "\\gamma", + "type": "inline_equation" + }, + { + "bbox": [ + 313, + 352, + 505, + 366 + ], + "score": 1.0, + "content": ". Some will see their generalization performance", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 364, + 505, + 376 + ], + "spans": [ + { + "bbox": [ + 105, + 364, + 505, + 376 + ], + "score": 1.0, + "content": "increase, while others will have decreasing generalization performances. We further confirm such", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 375, + 506, + 387 + ], + "spans": [ + { + "bbox": [ + 106, + 375, + 506, + 387 + ], + "score": 1.0, + "content": "findings in Figs. 9 and 10 for dropout where the same per-class trend is observed. In short, even for", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 384, + 506, + 399 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 506, + 399 + ], + "score": 1.0, + "content": "uninformative regularizers such as weight decay or dropout, a per-class bias is introduced, reducing", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 396, + 506, + 410 + ], + "spans": [ + { + "bbox": [ + 105, + 396, + 506, + 410 + ], + "score": 1.0, + "content": "performances for some of the classes. Although weight-decay is one of the most popular regularizer", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "score": 1.0, + "content": "that is uninformed on the data and task at hand, recent studies have demonstrated that techniques", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 418, + 506, + 430 + ], + "spans": [ + { + "bbox": [ + 106, + 418, + 506, + 430 + ], + "score": 1.0, + "content": "such as model pruning —which can be seen as a post-training model complexity reduction i.e.", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 429, + 507, + 442 + ], + "spans": [ + { + "bbox": [ + 105, + 429, + 507, + 442 + ], + "score": 1.0, + "content": "regularization— also produce increased bias towards under-represented features [Hooker et al., 2019,", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 440, + 505, + 452 + ], + "spans": [ + { + "bbox": [ + 106, + 440, + 505, + 452 + ], + "score": 1.0, + "content": "2020]. More recently, Balestriero et al. [2022] obtained the close-form explicit regularizer of DA", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 451, + 505, + 464 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 505, + 464 + ], + "score": 1.0, + "content": "from which it is possible to quantify the sample-dependent aspect of DA’s regularization. From our", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 461, + 505, + 475 + ], + "spans": [ + { + "bbox": [ + 105, + 461, + 505, + 475 + ], + "score": 1.0, + "content": "findings, it seems that classes sharing the same type of features are thus impacted different by DAs.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 17.5 + }, + { + "type": "text", + "bbox": [ + 107, + 478, + 505, + 621 + ], + "lines": [ + { + "bbox": [ + 105, + 477, + 506, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 477, + 506, + 491 + ], + "score": 1.0, + "content": "Formal Statistical Test. To further convey our claim, we now propose a formal statistical test", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 488, + 506, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 488, + 506, + 502 + ], + "score": 1.0, + "content": "[Neyman and Pearson, 1933, Fisher, 1955] on the hypothesis that the per-class accuracy is significantly", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 500, + 506, + 513 + ], + "spans": [ + { + "bbox": [ + 105, + 500, + 506, + 513 + ], + "score": 1.0, + "content": "higher when DA is applied for each class (details provided in Appendix A.2). We obtain that there is", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 510, + 505, + 523 + ], + "spans": [ + { + "bbox": [ + 105, + 510, + 291, + 523 + ], + "score": 1.0, + "content": "enough evidence to reject the hypothesis with", + "type": "text" + }, + { + "bbox": [ + 292, + 511, + 312, + 522 + ], + "score": 0.89, + "content": "9 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 312, + 510, + 373, + 523 + ], + "score": 1.0, + "content": "confidence for", + "type": "text" + }, + { + "bbox": [ + 373, + 511, + 396, + 522 + ], + "score": 0.89, + "content": "4 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 396, + 510, + 505, + 523 + ], + "score": 1.0, + "content": "of all the classes, and with", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 521, + 506, + 535 + ], + "spans": [ + { + "bbox": [ + 106, + 522, + 126, + 533 + ], + "score": 0.87, + "content": "9 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 127, + 521, + 185, + 535 + ], + "score": 1.0, + "content": "confidence for", + "type": "text" + }, + { + "bbox": [ + 185, + 522, + 208, + 532 + ], + "score": 0.89, + "content": "2 . { \\dot { 6 } } \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 208, + 521, + 506, + 535 + ], + "score": 1.0, + "content": "of all the classes. Hence there is sufficient evidence to say that the per-class", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 533, + 505, + 546 + ], + "spans": [ + { + "bbox": [ + 105, + 533, + 341, + 546 + ], + "score": 1.0, + "content": "test accuracies is not increased when introducing DA for", + "type": "text" + }, + { + "bbox": [ + 341, + 533, + 363, + 543 + ], + "score": 0.88, + "content": "4 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 364, + 533, + 505, + 546 + ], + "score": 1.0, + "content": "of the 1000 Imagenet classes. We", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 543, + 506, + 557 + ], + "spans": [ + { + "bbox": [ + 105, + 543, + 506, + 557 + ], + "score": 1.0, + "content": "provide in Table 1 the same statistical test but applied on a variety of settings including different", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 554, + 505, + 567 + ], + "spans": [ + { + "bbox": [ + 105, + 554, + 505, + 567 + ], + "score": 1.0, + "content": "architectures (resnet50, densenet121, ViT-small and ConvNext-Tiny) and across the random crop DA", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 565, + 505, + 578 + ], + "spans": [ + { + "bbox": [ + 105, + 565, + 505, + 578 + ], + "score": 1.0, + "content": "and the weight decay controlled experiments. We should highlight however that this is not necessarily", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 576, + 505, + 589 + ], + "spans": [ + { + "bbox": [ + 105, + 576, + 505, + 589 + ], + "score": 1.0, + "content": "a meaningful measure since for example one regularization might not have any negative impact on the", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 587, + 505, + 600 + ], + "spans": [ + { + "bbox": [ + 105, + 587, + 505, + 600 + ], + "score": 1.0, + "content": "classes, but can provide a beneficial gain that is drastically different between classes. Hence, although", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 596, + 506, + 613 + ], + "spans": [ + { + "bbox": [ + 105, + 596, + 506, + 613 + ], + "score": 1.0, + "content": "the per-class performance does not drop by introduce the regularizer, the inter-class performance gap", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 609, + 344, + 622 + ], + "spans": [ + { + "bbox": [ + 105, + 609, + 344, + 622 + ], + "score": 1.0, + "content": "can be increased by it, which is an equally harmful impact.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 34 + }, + { + "type": "text", + "bbox": [ + 108, + 625, + 502, + 658 + ], + "lines": [ + { + "bbox": [ + 107, + 625, + 504, + 637 + ], + "spans": [ + { + "bbox": [ + 107, + 625, + 504, + 637 + ], + "score": 1.0, + "content": "The next Section 2.2 proposes to study the scenario of introducing a pre-trained model, on a different", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 636, + 504, + 648 + ], + "spans": [ + { + "bbox": [ + 106, + 636, + 504, + 648 + ], + "score": 1.0, + "content": "downstream task to show that the class-dependent effect of regularization remains present and unfair", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 647, + 257, + 660 + ], + "spans": [ + { + "bbox": [ + 106, + 647, + 257, + 660 + ], + "score": 1.0, + "content": "towards specific downstream classes.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 42 + }, + { + "type": "title", + "bbox": [ + 108, + 671, + 407, + 682 + ], + "lines": [ + { + "bbox": [ + 105, + 670, + 408, + 684 + ], + "spans": [ + { + "bbox": [ + 105, + 670, + 408, + 684 + ], + "score": 1.0, + "content": "2.2 The Class-Dependent Bias Transfers to Other Downstream Tasks", + "type": "text" + } + ], + "index": 44 + } + ], + "index": 44 + }, + { + "type": "text", + "bbox": [ + 108, + 689, + 504, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 688, + 506, + 702 + ], + "spans": [ + { + "bbox": [ + 106, + 688, + 506, + 702 + ], + "score": 1.0, + "content": "The last experiment we propose is to quantify the amount of class-dependent bias that transfers to", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "other downstream tasks, a common situation in transfer learning and in system deployment to the", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 711, + 506, + 724 + ], + "spans": [ + { + "bbox": [ + 105, + 711, + 506, + 724 + ], + "score": 1.0, + "content": "real world [Pan and Yang, 2009]. We thus want to measure how regularization applied during the", + "type": "text" + } + ], + "index": 47 + } + ], + "index": 46 + } + ], + "page_idx": 3, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 742, + 308, + 750 + ], + "lines": [ + { + "bbox": [ + 301, + 741, + 310, + 752 + ], + "spans": [ + { + "bbox": [ + 301, + 741, + 310, + 752 + ], + "score": 1.0, + "content": "", + "type": "text", + "height": 11, + "width": 9 + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "image", + "bbox": [ + 107, + 70, + 501, + 190 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 107, + 70, + 501, + 190 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 107, + 70, + 501, + 190 + ], + "spans": [ + { + "bbox": [ + 107, + 70, + 501, + 190 + ], + "score": 0.971, + "type": "image", + "image_path": "a9f2b68ae848c586ddff72ac17bb6397ed8cefb20460c7f6deba497956f957c6.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 107, + 70, + 501, + 110.0 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 107, + 110.0, + 501, + 150.0 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 107, + 150.0, + 501, + 190.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 191, + 505, + 240 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 189, + 505, + 202 + ], + "spans": [ + { + "bbox": [ + 105, + 189, + 505, + 202 + ], + "score": 1.0, + "content": "Figure 3: Varying the amount of weight decay, an uninformed regularizer employed throughout, surprisingly", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 200, + 506, + 212 + ], + "spans": [ + { + "bbox": [ + 106, + 200, + 506, + 212 + ], + "score": 1.0, + "content": "exhibits similar class-dependent bias as the DA scenario of Fig. 2. Images for each class are provided in Fig. 13,", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 210, + 505, + 222 + ], + "spans": [ + { + "bbox": [ + 106, + 210, + 505, + 222 + ], + "score": 1.0, + "content": "in the appendix. Results obtained by averaging over 20 runs, official PyTorch resnet50 implementation trained", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 220, + 505, + 231 + ], + "spans": [ + { + "bbox": [ + 106, + 220, + 505, + 231 + ], + "score": 1.0, + "content": "on Imagenet with varying weight decay, see Fig. 16 for DenseNet121 results with the same trend and Fig. 8 for", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 230, + 235, + 243 + ], + "spans": [ + { + "bbox": [ + 106, + 230, + 235, + 243 + ], + "score": 1.0, + "content": "the convnext and ViT experiments.", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 5 + } + ], + "index": 3.0 + }, + { + "type": "text", + "bbox": [ + 107, + 254, + 505, + 473 + ], + "lines": [ + { + "bbox": [ + 106, + 254, + 506, + 268 + ], + "spans": [ + { + "bbox": [ + 106, + 254, + 506, + 268 + ], + "score": 1.0, + "content": "Uninformed Regularization. As per the arguments given in Sections 3.1 and 3.2, it would be", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 266, + 506, + 278 + ], + "spans": [ + { + "bbox": [ + 105, + 266, + 506, + 278 + ], + "score": 1.0, + "content": "natural to assume that what makes DA responsible for creating class-dependent bias in DNNs is", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 275, + 506, + 290 + ], + "spans": [ + { + "bbox": [ + 105, + 275, + 506, + 290 + ], + "score": 1.0, + "content": "our misfortune in defining correct augmentation policies. Hence, uninformed weight decay or", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 286, + 505, + 300 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 505, + 300 + ], + "score": 1.0, + "content": "dropout should behave differently and more fairly. We demonstrate here that such regularizers are", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 297, + 506, + 312 + ], + "spans": [ + { + "bbox": [ + 105, + 297, + 506, + 312 + ], + "score": 1.0, + "content": "also unfair between classes. We thus propose to train multiple models with varying weight-decay", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 308, + 506, + 322 + ], + "spans": [ + { + "bbox": [ + 105, + 308, + 506, + 322 + ], + "score": 1.0, + "content": "and dropout parameters. In our setting, weight-decay is applied to all the DNN parameters except", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 320, + 506, + 333 + ], + "spans": [ + { + "bbox": [ + 106, + 320, + 506, + 333 + ], + "score": 1.0, + "content": "for the ones of batch-normalization layers, as commonly done [Hastie et al., 2009, Leclerc et al.,", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 329, + 506, + 345 + ], + "spans": [ + { + "bbox": [ + 105, + 329, + 506, + 345 + ], + "score": 1.0, + "content": "2022]. We report in Fig. 3 the per-class performance of a resnet50 trained on Imagenet with varying", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 342, + 505, + 354 + ], + "spans": [ + { + "bbox": [ + 106, + 342, + 205, + 354 + ], + "score": 1.0, + "content": "weight decay coefficient", + "type": "text" + }, + { + "bbox": [ + 205, + 343, + 213, + 353 + ], + "score": 0.82, + "content": "\\gamma", + "type": "inline_equation" + }, + { + "bbox": [ + 213, + 342, + 505, + 354 + ], + "score": 1.0, + "content": "(as was done for DA in Fig. 2) and we observe that different classes have", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 352, + 505, + 366 + ], + "spans": [ + { + "bbox": [ + 105, + 352, + 305, + 366 + ], + "score": 1.0, + "content": "different test accuracy sensitivities to variations in", + "type": "text" + }, + { + "bbox": [ + 306, + 354, + 313, + 364 + ], + "score": 0.79, + "content": "\\gamma", + "type": "inline_equation" + }, + { + "bbox": [ + 313, + 352, + 505, + 366 + ], + "score": 1.0, + "content": ". Some will see their generalization performance", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 364, + 505, + 376 + ], + "spans": [ + { + "bbox": [ + 105, + 364, + 505, + 376 + ], + "score": 1.0, + "content": "increase, while others will have decreasing generalization performances. We further confirm such", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 375, + 506, + 387 + ], + "spans": [ + { + "bbox": [ + 106, + 375, + 506, + 387 + ], + "score": 1.0, + "content": "findings in Figs. 9 and 10 for dropout where the same per-class trend is observed. In short, even for", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 384, + 506, + 399 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 506, + 399 + ], + "score": 1.0, + "content": "uninformative regularizers such as weight decay or dropout, a per-class bias is introduced, reducing", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 396, + 506, + 410 + ], + "spans": [ + { + "bbox": [ + 105, + 396, + 506, + 410 + ], + "score": 1.0, + "content": "performances for some of the classes. Although weight-decay is one of the most popular regularizer", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "spans": [ + { + "bbox": [ + 105, + 407, + 505, + 420 + ], + "score": 1.0, + "content": "that is uninformed on the data and task at hand, recent studies have demonstrated that techniques", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 418, + 506, + 430 + ], + "spans": [ + { + "bbox": [ + 106, + 418, + 506, + 430 + ], + "score": 1.0, + "content": "such as model pruning —which can be seen as a post-training model complexity reduction i.e.", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 429, + 507, + 442 + ], + "spans": [ + { + "bbox": [ + 105, + 429, + 507, + 442 + ], + "score": 1.0, + "content": "regularization— also produce increased bias towards under-represented features [Hooker et al., 2019,", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 440, + 505, + 452 + ], + "spans": [ + { + "bbox": [ + 106, + 440, + 505, + 452 + ], + "score": 1.0, + "content": "2020]. More recently, Balestriero et al. [2022] obtained the close-form explicit regularizer of DA", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 451, + 505, + 464 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 505, + 464 + ], + "score": 1.0, + "content": "from which it is possible to quantify the sample-dependent aspect of DA’s regularization. From our", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 461, + 505, + 475 + ], + "spans": [ + { + "bbox": [ + 105, + 461, + 505, + 475 + ], + "score": 1.0, + "content": "findings, it seems that classes sharing the same type of features are thus impacted different by DAs.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 17.5, + "bbox_fs": [ + 105, + 254, + 507, + 475 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 478, + 505, + 621 + ], + "lines": [ + { + "bbox": [ + 105, + 477, + 506, + 491 + ], + "spans": [ + { + "bbox": [ + 105, + 477, + 506, + 491 + ], + "score": 1.0, + "content": "Formal Statistical Test. To further convey our claim, we now propose a formal statistical test", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 488, + 506, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 488, + 506, + 502 + ], + "score": 1.0, + "content": "[Neyman and Pearson, 1933, Fisher, 1955] on the hypothesis that the per-class accuracy is significantly", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 500, + 506, + 513 + ], + "spans": [ + { + "bbox": [ + 105, + 500, + 506, + 513 + ], + "score": 1.0, + "content": "higher when DA is applied for each class (details provided in Appendix A.2). We obtain that there is", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 510, + 505, + 523 + ], + "spans": [ + { + "bbox": [ + 105, + 510, + 291, + 523 + ], + "score": 1.0, + "content": "enough evidence to reject the hypothesis with", + "type": "text" + }, + { + "bbox": [ + 292, + 511, + 312, + 522 + ], + "score": 0.89, + "content": "9 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 312, + 510, + 373, + 523 + ], + "score": 1.0, + "content": "confidence for", + "type": "text" + }, + { + "bbox": [ + 373, + 511, + 396, + 522 + ], + "score": 0.89, + "content": "4 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 396, + 510, + 505, + 523 + ], + "score": 1.0, + "content": "of all the classes, and with", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 521, + 506, + 535 + ], + "spans": [ + { + "bbox": [ + 106, + 522, + 126, + 533 + ], + "score": 0.87, + "content": "9 9 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 127, + 521, + 185, + 535 + ], + "score": 1.0, + "content": "confidence for", + "type": "text" + }, + { + "bbox": [ + 185, + 522, + 208, + 532 + ], + "score": 0.89, + "content": "2 . { \\dot { 6 } } \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 208, + 521, + 506, + 535 + ], + "score": 1.0, + "content": "of all the classes. Hence there is sufficient evidence to say that the per-class", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 533, + 505, + 546 + ], + "spans": [ + { + "bbox": [ + 105, + 533, + 341, + 546 + ], + "score": 1.0, + "content": "test accuracies is not increased when introducing DA for", + "type": "text" + }, + { + "bbox": [ + 341, + 533, + 363, + 543 + ], + "score": 0.88, + "content": "4 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 364, + 533, + 505, + 546 + ], + "score": 1.0, + "content": "of the 1000 Imagenet classes. We", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 543, + 506, + 557 + ], + "spans": [ + { + "bbox": [ + 105, + 543, + 506, + 557 + ], + "score": 1.0, + "content": "provide in Table 1 the same statistical test but applied on a variety of settings including different", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 554, + 505, + 567 + ], + "spans": [ + { + "bbox": [ + 105, + 554, + 505, + 567 + ], + "score": 1.0, + "content": "architectures (resnet50, densenet121, ViT-small and ConvNext-Tiny) and across the random crop DA", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 565, + 505, + 578 + ], + "spans": [ + { + "bbox": [ + 105, + 565, + 505, + 578 + ], + "score": 1.0, + "content": "and the weight decay controlled experiments. We should highlight however that this is not necessarily", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 576, + 505, + 589 + ], + "spans": [ + { + "bbox": [ + 105, + 576, + 505, + 589 + ], + "score": 1.0, + "content": "a meaningful measure since for example one regularization might not have any negative impact on the", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 587, + 505, + 600 + ], + "spans": [ + { + "bbox": [ + 105, + 587, + 505, + 600 + ], + "score": 1.0, + "content": "classes, but can provide a beneficial gain that is drastically different between classes. Hence, although", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 596, + 506, + 613 + ], + "spans": [ + { + "bbox": [ + 105, + 596, + 506, + 613 + ], + "score": 1.0, + "content": "the per-class performance does not drop by introduce the regularizer, the inter-class performance gap", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 609, + 344, + 622 + ], + "spans": [ + { + "bbox": [ + 105, + 609, + 344, + 622 + ], + "score": 1.0, + "content": "can be increased by it, which is an equally harmful impact.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 34, + "bbox_fs": [ + 105, + 477, + 506, + 622 + ] + }, + { + "type": "text", + "bbox": [ + 108, + 625, + 502, + 658 + ], + "lines": [ + { + "bbox": [ + 107, + 625, + 504, + 637 + ], + "spans": [ + { + "bbox": [ + 107, + 625, + 504, + 637 + ], + "score": 1.0, + "content": "The next Section 2.2 proposes to study the scenario of introducing a pre-trained model, on a different", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 636, + 504, + 648 + ], + "spans": [ + { + "bbox": [ + 106, + 636, + 504, + 648 + ], + "score": 1.0, + "content": "downstream task to show that the class-dependent effect of regularization remains present and unfair", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 106, + 647, + 257, + 660 + ], + "spans": [ + { + "bbox": [ + 106, + 647, + 257, + 660 + ], + "score": 1.0, + "content": "towards specific downstream classes.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 42, + "bbox_fs": [ + 106, + 625, + 504, + 660 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 671, + 407, + 682 + ], + "lines": [ + { + "bbox": [ + 105, + 670, + 408, + 684 + ], + "spans": [ + { + "bbox": [ + 105, + 670, + 408, + 684 + ], + "score": 1.0, + "content": "2.2 The Class-Dependent Bias Transfers to Other Downstream Tasks", + "type": "text" + } + ], + "index": 44 + } + ], + "index": 44 + }, + { + "type": "text", + "bbox": [ + 108, + 689, + 504, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 688, + 506, + 702 + ], + "spans": [ + { + "bbox": [ + 106, + 688, + 506, + 702 + ], + "score": 1.0, + "content": "The last experiment we propose is to quantify the amount of class-dependent bias that transfers to", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "other downstream tasks, a common situation in transfer learning and in system deployment to the", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 711, + 506, + 724 + ], + "spans": [ + { + "bbox": [ + 105, + 711, + 506, + 724 + ], + "score": 1.0, + "content": "real world [Pan and Yang, 2009]. We thus want to measure how regularization applied during the", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 415, + 505, + 429 + ], + "spans": [ + { + "bbox": [ + 105, + 415, + 505, + 429 + ], + "score": 1.0, + "content": "pre-training phase on a source dataset impacts the per-class accuracy of that model on the target", + "type": "text", + "cross_page": true + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 426, + 140, + 441 + ], + "spans": [ + { + "bbox": [ + 105, + 426, + 140, + 441 + ], + "score": 1.0, + "content": "dataset.", + "type": "text", + "cross_page": true + } + ], + "index": 18 + } + ], + "index": 46, + "bbox_fs": [ + 105, + 688, + 506, + 724 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "table", + "bbox": [ + 107, + 126, + 504, + 189 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 106, + 70, + 505, + 121 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 69, + 506, + 82 + ], + "spans": [ + { + "bbox": [ + 105, + 69, + 506, + 82 + ], + "score": 1.0, + "content": "Table 1: Percentage of Imagenet classes for which the test set performance (per-class) is statistically not greater", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 80, + 505, + 91 + ], + "spans": [ + { + "bbox": [ + 106, + 80, + 505, + 91 + ], + "score": 1.0, + "content": "when applying random crop DA (or weight decay) as measured by the statistical test from Section 2. We observe", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 89, + 506, + 102 + ], + "spans": [ + { + "bbox": [ + 105, + 89, + 506, + 102 + ], + "score": 1.0, + "content": "that although this measure is highly conservative since a regularizer (DA or weight decay) might now have a", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 100, + 506, + 113 + ], + "spans": [ + { + "bbox": [ + 105, + 100, + 506, + 113 + ], + "score": 1.0, + "content": "negative impact on a per-class performance but still increase the performance gap between difference classes,", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 109, + 502, + 123 + ], + "spans": [ + { + "bbox": [ + 105, + 109, + 502, + 123 + ], + "score": 1.0, + "content": "already, a nonzero proportion of classes are negatively impact by introduce random crop DA or weight decay.", + "type": "text" + } + ], + "index": 4 + } + ], + "index": 2 + }, + { + "type": "table_body", + "bbox": [ + 107, + 126, + 504, + 189 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 107, + 126, + 504, + 189 + ], + "spans": [ + { + "bbox": [ + 107, + 126, + 504, + 189 + ], + "score": 0.961, + "html": "
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Hence, selecting the pre-trained model with best average test accuracy", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 353, + 505, + 363 + ], + "spans": [ + { + "bbox": [ + 106, + 353, + 505, + 363 + ], + "score": 1.0, + "content": "on the source dataset might result in deploying a model with the worst performance on the classes of interest in", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 363, + 506, + 374 + ], + "spans": [ + { + "bbox": [ + 106, + 363, + 506, + 374 + ], + "score": 1.0, + "content": "the target task. Images for each class are provided in Fig. 17, in the appendix. Results obtained by averaging over", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 372, + 505, + 384 + ], + "spans": [ + { + "bbox": [ + 106, + 372, + 505, + 384 + ], + "score": 1.0, + "content": "20 runs, official PyTorch resnet50 implementation trained on Imagenet with varying random crop lower bound", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 382, + 336, + 393 + ], + "spans": [ + { + "bbox": [ + 105, + 382, + 336, + 393 + ], + "score": 1.0, + "content": "and transferred to INaturalist with frozen backbone parameters.", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 13.5 + } + ], + "index": 11.25 + }, + { + "type": "text", + "bbox": [ + 108, + 416, + 504, + 439 + ], + "lines": [ + { + "bbox": [ + 105, + 415, + 505, + 429 + ], + "spans": [ + { + "bbox": [ + 105, + 415, + 505, + 429 + ], + "score": 1.0, + "content": "pre-training phase on a source dataset impacts the per-class accuracy of that model on the target", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 426, + 140, + 441 + ], + "spans": [ + { + "bbox": [ + 105, + 426, + 140, + 441 + ], + "score": 1.0, + "content": "dataset.", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 17.5 + }, + { + "type": "text", + "bbox": [ + 106, + 443, + 505, + 554 + ], + "lines": [ + { + "bbox": [ + 106, + 444, + 505, + 456 + ], + "spans": [ + { + "bbox": [ + 106, + 444, + 505, + 456 + ], + "score": 1.0, + "content": "In order to keep the setting similar to Section 2.1, we adopt a resnet50 model with random crop DA.", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 455, + 506, + 468 + ], + "spans": [ + { + "bbox": [ + 105, + 455, + 415, + 468 + ], + "score": 1.0, + "content": "That model is pre-trained on Imagenet dataset (source) with varying value of", + "type": "text" + }, + { + "bbox": [ + 416, + 457, + 423, + 465 + ], + "score": 0.71, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 423, + 455, + 506, + 468 + ], + "score": 1.0, + "content": "(random crop lower", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 465, + 505, + 478 + ], + "spans": [ + { + "bbox": [ + 105, + 465, + 505, + 478 + ], + "score": 1.0, + "content": "bound) and then, the trained model is transferred to the INaturalist dataset [Van Horn et al., 2018]", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 106, + 477, + 505, + 489 + ], + "spans": [ + { + "bbox": [ + 106, + 477, + 505, + 489 + ], + "score": 1.0, + "content": "(target) that consists of 10,000 classes. When transferring the model to INaturalist, the parameters", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 488, + 506, + 500 + ], + "spans": [ + { + "bbox": [ + 106, + 488, + 506, + 500 + ], + "score": 1.0, + "content": "are kept frozen, and only a linear classifier is trained on top of it. We report in Fig. 4 the performance", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 497, + 506, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 497, + 250, + 511 + ], + "score": 1.0, + "content": "of the trained models with varying", + "type": "text" + }, + { + "bbox": [ + 250, + 500, + 258, + 509 + ], + "score": 0.72, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 258, + 497, + 506, + 511 + ], + "score": 1.0, + "content": "on different INaturalist classes. We observe once again that", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 508, + 506, + 523 + ], + "spans": [ + { + "bbox": [ + 105, + 508, + 506, + 523 + ], + "score": 1.0, + "content": "the best resnet50 —on average— is not necessarily the one that should be deployed as there exists a", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 520, + 506, + 533 + ], + "spans": [ + { + "bbox": [ + 106, + 520, + 252, + 533 + ], + "score": 1.0, + "content": "strong per-class bias that varies with", + "type": "text" + }, + { + "bbox": [ + 252, + 522, + 259, + 530 + ], + "score": 0.74, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 259, + 520, + 506, + 533 + ], + "score": 1.0, + "content": ". As a result, picking the best performing model from a source", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 531, + 505, + 544 + ], + "spans": [ + { + "bbox": [ + 106, + 531, + 505, + 544 + ], + "score": 1.0, + "content": "dataset to a target dataset, might leave the pipeline to perform poorly since that model might also be", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 541, + 421, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 421, + 555 + ], + "score": 1.0, + "content": "the one that is the most biased against the class of interest in the target dataset.", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 23.5 + }, + { + "type": "text", + "bbox": [ + 107, + 558, + 505, + 603 + ], + "lines": [ + { + "bbox": [ + 105, + 558, + 505, + 570 + ], + "spans": [ + { + "bbox": [ + 105, + 558, + 505, + 570 + ], + "score": 1.0, + "content": "This result should motivate the design of novel regularizers that do not reduce performances between", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 569, + 505, + 583 + ], + "spans": [ + { + "bbox": [ + 105, + 569, + 505, + 583 + ], + "score": 1.0, + "content": "classes at different regimes. Additionally, due to the cost of training multiple models with varying", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 581, + 506, + 592 + ], + "spans": [ + { + "bbox": [ + 105, + 581, + 506, + 592 + ], + "score": 1.0, + "content": "regularization settings, one might wonder on the possible alternative solutions to detect trends such", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 591, + 359, + 604 + ], + "spans": [ + { + "bbox": [ + 105, + 591, + 359, + 604 + ], + "score": 1.0, + "content": "as shown in Fig. 4 only when given a single pre-trained model.", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 30.5 + }, + { + "type": "title", + "bbox": [ + 108, + 613, + 483, + 640 + ], + "lines": [ + { + "bbox": [ + 104, + 612, + 484, + 628 + ], + "spans": [ + { + "bbox": [ + 104, + 612, + 484, + 628 + ], + "score": 1.0, + "content": "3 Understanding Why and When Regularization Produces Models With", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 123, + 627, + 277, + 642 + ], + "spans": [ + { + "bbox": [ + 123, + 627, + 277, + 642 + ], + "score": 1.0, + "content": "Class-Dependent Preferences", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 33.5 + }, + { + "type": "text", + "bbox": [ + 107, + 645, + 506, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 645, + 506, + 658 + ], + "spans": [ + { + "bbox": [ + 106, + 645, + 506, + 658 + ], + "score": 1.0, + "content": "The first part of our study (Section 2) empirically validated that DNN regularization produces unfair", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 656, + 506, + 669 + ], + "spans": [ + { + "bbox": [ + 106, + 656, + 506, + 669 + ], + "score": 1.0, + "content": "model complexity control over different classes, resulting in a model performing poorly on a few of", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 667, + 506, + 679 + ], + "spans": [ + { + "bbox": [ + 105, + 667, + 506, + 679 + ], + "score": 1.0, + "content": "the classes although being highly performing on average. We now provide in Sections 3.1 and 3.2", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 678, + 506, + 690 + ], + "spans": [ + { + "bbox": [ + 105, + 678, + 506, + 690 + ], + "score": 1.0, + "content": "some intuition on why DA can be a source of bias regardless of the task, dataset and model at hand.", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 688, + 506, + 702 + ], + "spans": [ + { + "bbox": [ + 105, + 688, + 506, + 702 + ], + "score": 1.0, + "content": "Then, Section 3.3 reviews existing works trying to confront regularization and model bias, and as we", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 699, + 505, + 713 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 505, + 713 + ], + "score": 1.0, + "content": "will see, an out-of-the-box solution does not seem to exist when there are only a few classes suffering", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 711, + 243, + 723 + ], + "spans": [ + { + "bbox": [ + 106, + 711, + 243, + 723 + ], + "score": 1.0, + "content": "from regularization (Section 3.4).", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 38 + } + ], + "page_idx": 4, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 741, + 308, + 750 + ], + "lines": [ + { + "bbox": [ + 302, + 740, + 309, + 753 + ], + "spans": [ + { + "bbox": [ + 302, + 740, + 309, + 753 + ], + "score": 1.0, + "content": "5", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "table", + "bbox": [ + 107, + 126, + 504, + 189 + ], + "blocks": [ + { + "type": "table_caption", + "bbox": [ + 106, + 70, + 505, + 121 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 69, + 506, + 82 + ], + "spans": [ + { + "bbox": [ + 105, + 69, + 506, + 82 + ], + "score": 1.0, + "content": "Table 1: Percentage of Imagenet classes for which the test set performance (per-class) is statistically not greater", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 80, + 505, + 91 + ], + "spans": [ + { + "bbox": [ + 106, + 80, + 505, + 91 + ], + "score": 1.0, + "content": "when applying random crop DA (or weight decay) as measured by the statistical test from Section 2. We observe", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 89, + 506, + 102 + ], + "spans": [ + { + "bbox": [ + 105, + 89, + 506, + 102 + ], + "score": 1.0, + "content": "that although this measure is highly conservative since a regularizer (DA or weight decay) might now have a", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 100, + 506, + 113 + ], + "spans": [ + { + "bbox": [ + 105, + 100, + 506, + 113 + ], + "score": 1.0, + "content": "negative impact on a per-class performance but still increase the performance gap between difference classes,", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 109, + 502, + 123 + ], + "spans": [ + { + "bbox": [ + 105, + 109, + 502, + 123 + ], + "score": 1.0, + "content": "already, a nonzero proportion of classes are negatively impact by introduce random crop DA or weight decay.", + "type": "text" + } + ], + "index": 4 + } + ], + "index": 2 + }, + { + "type": "table_body", + "bbox": [ + 107, + 126, + 504, + 189 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 107, + 126, + 504, + 189 + ], + "spans": [ + { + "bbox": [ + 107, + 126, + 504, + 189 + ], + "score": 0.961, + "html": "
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Hence, selecting the pre-trained model with best average test accuracy", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 353, + 505, + 363 + ], + "spans": [ + { + "bbox": [ + 106, + 353, + 505, + 363 + ], + "score": 1.0, + "content": "on the source dataset might result in deploying a model with the worst performance on the classes of interest in", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 363, + 506, + 374 + ], + "spans": [ + { + "bbox": [ + 106, + 363, + 506, + 374 + ], + "score": 1.0, + "content": "the target task. Images for each class are provided in Fig. 17, in the appendix. Results obtained by averaging over", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 106, + 372, + 505, + 384 + ], + "spans": [ + { + "bbox": [ + 106, + 372, + 505, + 384 + ], + "score": 1.0, + "content": "20 runs, official PyTorch resnet50 implementation trained on Imagenet with varying random crop lower bound", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 382, + 336, + 393 + ], + "spans": [ + { + "bbox": [ + 105, + 382, + 336, + 393 + ], + "score": 1.0, + "content": "and transferred to INaturalist with frozen backbone parameters.", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 13.5 + } + ], + "index": 11.25 + }, + { + "type": "text", + "bbox": [ + 108, + 416, + 504, + 439 + ], + "lines": [], + "index": 17.5, + "bbox_fs": [ + 105, + 415, + 505, + 441 + ], + "lines_deleted": true + }, + { + "type": "text", + "bbox": [ + 106, + 443, + 505, + 554 + ], + "lines": [ + { + "bbox": [ + 106, + 444, + 505, + 456 + ], + "spans": [ + { + "bbox": [ + 106, + 444, + 505, + 456 + ], + "score": 1.0, + "content": "In order to keep the setting similar to Section 2.1, we adopt a resnet50 model with random crop DA.", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 455, + 506, + 468 + ], + "spans": [ + { + "bbox": [ + 105, + 455, + 415, + 468 + ], + "score": 1.0, + "content": "That model is pre-trained on Imagenet dataset (source) with varying value of", + "type": "text" + }, + { + "bbox": [ + 416, + 457, + 423, + 465 + ], + "score": 0.71, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 423, + 455, + 506, + 468 + ], + "score": 1.0, + "content": "(random crop lower", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 465, + 505, + 478 + ], + "spans": [ + { + "bbox": [ + 105, + 465, + 505, + 478 + ], + "score": 1.0, + "content": "bound) and then, the trained model is transferred to the INaturalist dataset [Van Horn et al., 2018]", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 106, + 477, + 505, + 489 + ], + "spans": [ + { + "bbox": [ + 106, + 477, + 505, + 489 + ], + "score": 1.0, + "content": "(target) that consists of 10,000 classes. When transferring the model to INaturalist, the parameters", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 488, + 506, + 500 + ], + "spans": [ + { + "bbox": [ + 106, + 488, + 506, + 500 + ], + "score": 1.0, + "content": "are kept frozen, and only a linear classifier is trained on top of it. We report in Fig. 4 the performance", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 497, + 506, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 497, + 250, + 511 + ], + "score": 1.0, + "content": "of the trained models with varying", + "type": "text" + }, + { + "bbox": [ + 250, + 500, + 258, + 509 + ], + "score": 0.72, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 258, + 497, + 506, + 511 + ], + "score": 1.0, + "content": "on different INaturalist classes. We observe once again that", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 508, + 506, + 523 + ], + "spans": [ + { + "bbox": [ + 105, + 508, + 506, + 523 + ], + "score": 1.0, + "content": "the best resnet50 —on average— is not necessarily the one that should be deployed as there exists a", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 520, + 506, + 533 + ], + "spans": [ + { + "bbox": [ + 106, + 520, + 252, + 533 + ], + "score": 1.0, + "content": "strong per-class bias that varies with", + "type": "text" + }, + { + "bbox": [ + 252, + 522, + 259, + 530 + ], + "score": 0.74, + "content": "\\tau", + "type": "inline_equation" + }, + { + "bbox": [ + 259, + 520, + 506, + 533 + ], + "score": 1.0, + "content": ". As a result, picking the best performing model from a source", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 531, + 505, + 544 + ], + "spans": [ + { + "bbox": [ + 106, + 531, + 505, + 544 + ], + "score": 1.0, + "content": "dataset to a target dataset, might leave the pipeline to perform poorly since that model might also be", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 541, + 421, + 555 + ], + "spans": [ + { + "bbox": [ + 105, + 541, + 421, + 555 + ], + "score": 1.0, + "content": "the one that is the most biased against the class of interest in the target dataset.", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 23.5, + "bbox_fs": [ + 105, + 444, + 506, + 555 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 558, + 505, + 603 + ], + "lines": [ + { + "bbox": [ + 105, + 558, + 505, + 570 + ], + "spans": [ + { + "bbox": [ + 105, + 558, + 505, + 570 + ], + "score": 1.0, + "content": "This result should motivate the design of novel regularizers that do not reduce performances between", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 569, + 505, + 583 + ], + "spans": [ + { + "bbox": [ + 105, + 569, + 505, + 583 + ], + "score": 1.0, + "content": "classes at different regimes. 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Given a sample", + "type": "text" + }, + { + "bbox": [ + 257, + 153, + 290, + 163 + ], + "score": 0.9, + "content": "\\textbf { \\em x } \\in { \\mathcal { X } }", + "type": "inline_equation" + }, + { + "bbox": [ + 290, + 151, + 313, + 165 + ], + "score": 1.0, + "content": "with", + "type": "text" + }, + { + "bbox": [ + 314, + 152, + 355, + 163 + ], + "score": 0.92, + "content": "\\boldsymbol { \\mathcal { X } } \\subset \\mathbb { R } ^ { D }", + "type": "inline_equation" + }, + { + "bbox": [ + 355, + 151, + 414, + 165 + ], + "score": 1.0, + "content": ", we consider", + "type": "text" + }, + { + "bbox": [ + 414, + 152, + 463, + 165 + ], + "score": 0.92, + "content": "{ \\pmb y } \\triangleq f ^ { * } ( { \\pmb x } )", + "type": "inline_equation" + }, + { + "bbox": [ + 463, + 151, + 505, + 165 + ], + "score": 1.0, + "content": "to be the", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 163, + 506, + 177 + ], + "spans": [ + { + "bbox": [ + 105, + 163, + 380, + 177 + ], + "score": 1.0, + "content": "ground-truth target value. Hence our hope is to learn an approximator", + "type": "text" + }, + { + "bbox": [ + 380, + 164, + 390, + 175 + ], + "score": 0.87, + "content": "f _ { \\theta }", + "type": "inline_equation" + }, + { + "bbox": [ + 390, + 163, + 506, + 177 + ], + "score": 1.0, + "content": "that is as close as possible to", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 107, + 174, + 506, + 189 + ], + "spans": [ + { + "bbox": [ + 107, + 176, + 118, + 187 + ], + "score": 0.88, + "content": "f ^ { * }", + "type": "inline_equation" + }, + { + "bbox": [ + 118, + 174, + 178, + 189 + ], + "score": 1.0, + "content": "everywhere in", + "type": "text" + }, + { + "bbox": [ + 179, + 176, + 189, + 186 + ], + "score": 0.78, + "content": "\\mathcal { X }", + "type": "inline_equation" + }, + { + "bbox": [ + 189, + 174, + 375, + 189 + ], + "score": 1.0, + "content": ", although we only observe a finite training set", + "type": "text" + }, + { + "bbox": [ + 376, + 175, + 503, + 187 + ], + "score": 0.89, + "content": "\\mathbb { X } \\triangleq \\{ ( \\pmb { x } _ { 1 } , \\pmb { y } _ { 1 } ) , \\dots , ( \\pmb { x } _ { N } , \\pmb { y } _ { N } ) \\}", + "type": "inline_equation" + }, + { + "bbox": [ + 504, + 174, + 506, + 189 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 186, + 496, + 200 + ], + "spans": [ + { + "bbox": [ + 106, + 186, + 200, + 200 + ], + "score": 1.0, + "content": "Given an output vector", + "type": "text" + }, + { + "bbox": [ + 200, + 189, + 208, + 197 + ], + "score": 0.67, + "content": "\\textbf { \\em u }", + "type": "inline_equation" + }, + { + "bbox": [ + 209, + 186, + 374, + 200 + ], + "score": 1.0, + "content": "we also define the level-set of a mapping", + "type": "text" + }, + { + "bbox": [ + 375, + 187, + 382, + 199 + ], + "score": 0.84, + "content": "f", + "type": "inline_equation" + }, + { + "bbox": [ + 382, + 186, + 406, + 200 + ], + "score": 1.0, + "content": "to be", + "type": "text" + }, + { + "bbox": [ + 406, + 187, + 492, + 198 + ], + "score": 0.88, + "content": "\\{ { \\pmb x } \\in { \\mathcal { X } } : f ( { \\pmb x } ) = { \\pmb u } \\}", + "type": "inline_equation" + }, + { + "bbox": [ + 492, + 186, + 496, + 200 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 7.5 + }, + { + "type": "text", + "bbox": [ + 107, + 198, + 505, + 241 + ], + "lines": [ + { + "bbox": [ + 105, + 196, + 506, + 210 + ], + "spans": [ + { + "bbox": [ + 105, + 196, + 401, + 210 + ], + "score": 1.0, + "content": "Data-Augmentation notations. Additionally, one employs a DA policy", + "type": "text" + }, + { + "bbox": [ + 401, + 198, + 482, + 208 + ], + "score": 0.83, + "content": "\\mathcal { T } : \\mathbb { R } ^ { D } \\times \\mathcal { K } \\mapsto \\mathbb { R } ^ { D }", + "type": "inline_equation" + }, + { + "bbox": [ + 482, + 196, + 506, + 210 + ], + "score": 1.0, + "content": "such", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 208, + 505, + 221 + ], + "spans": [ + { + "bbox": [ + 106, + 208, + 263, + 220 + ], + "score": 1.0, + "content": "that given a transformation parameter", + "type": "text" + }, + { + "bbox": [ + 263, + 209, + 292, + 219 + ], + "score": 0.89, + "content": "\\alpha \\in { \\mathcal { K } }", + "type": "inline_equation" + }, + { + "bbox": [ + 292, + 208, + 296, + 220 + ], + "score": 1.0, + "content": ",", + "type": "text" + }, + { + "bbox": [ + 296, + 208, + 322, + 221 + ], + "score": 0.85, + "content": "{ \\mathcal T } _ { \\alpha } ( { \\pmb x } )", + "type": "inline_equation" + }, + { + "bbox": [ + 323, + 208, + 465, + 220 + ], + "score": 1.0, + "content": "produces the transformed view of", + "type": "text" + }, + { + "bbox": [ + 465, + 210, + 473, + 218 + ], + "score": 0.72, + "content": "_ { \\textbf { \\em x } }", + "type": "inline_equation" + }, + { + "bbox": [ + 473, + 208, + 505, + 220 + ], + "score": 1.0, + "content": ". 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Whenever the transformations produced by", + "type": "text" + }, + { + "bbox": [ + 328, + 244, + 357, + 255 + ], + "score": 0.85, + "content": "\\mathcal { T } _ { \\alpha } , \\forall \\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 357, + 242, + 473, + 257 + ], + "score": 1.0, + "content": "do not respect the level-set of", + "type": "text" + }, + { + "bbox": [ + 474, + 244, + 484, + 255 + ], + "score": 0.89, + "content": "f ^ { * }", + "type": "inline_equation" + }, + { + "bbox": [ + 485, + 242, + 506, + 257 + ], + "score": 1.0, + "content": ", and", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 254, + 505, + 267 + ], + "spans": [ + { + "bbox": [ + 105, + 254, + 505, + 267 + ], + "score": 1.0, + "content": "whenever the model has enough capacity to minimize the training loss, the DA will create irreducible", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 264, + 169, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 264, + 135, + 277 + ], + "score": 1.0, + "content": "bias in", + "type": "text" + }, + { + "bbox": [ + 136, + 265, + 146, + 276 + ], + "score": 0.87, + "content": "f _ { \\theta }", + "type": "inline_equation" + }, + { + "bbox": [ + 146, + 264, + 169, + 277 + ], + "score": 1.0, + "content": "as in", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 15 + }, + { + "type": "image", + "bbox": [ + 111, + 280, + 486, + 324 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 111, + 280, + 486, + 324 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 111, + 280, + 486, + 324 + ], + "spans": [ + { + "bbox": [ + 111, + 280, + 486, + 324 + ], + "score": 0.798, + "type": "image", + "image_path": "2ef5e8d9a430737307b40dbef9e6c37e868afc1ea40042ca3717c1fd319a031f.jpg" + } + ] + } + ], + "index": 18, + "virtual_lines": [ + { + "bbox": [ + 111, + 280, + 486, + 294.6666666666667 + ], + "spans": [], + "index": 17 + }, + { + "bbox": [ + 111, + 294.6666666666667, + 486, + 309.33333333333337 + ], + "spans": [], + "index": 18 + }, + { + "bbox": [ + 111, + 309.33333333333337, + 486, + 324.00000000000006 + ], + "spans": [], + "index": 19 + } + ] + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 107, + 333, + 505, + 367 + ], + "lines": [ + { + "bbox": [ + 105, + 334, + 506, + 346 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 314, + 346 + ], + "score": 1.0, + "content": "The main idea of the proof, provided in Appendix", + "type": "text" + }, + { + "bbox": [ + 314, + 334, + 322, + 344 + ], + "score": 0.27, + "content": "\\mathbf { B }", + "type": "inline_equation" + }, + { + "bbox": [ + 322, + 334, + 506, + 346 + ], + "score": 1.0, + "content": ", is to show that if a transformation does not", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 345, + 506, + 357 + ], + "spans": [ + { + "bbox": [ + 105, + 345, + 444, + 357 + ], + "score": 1.0, + "content": "move samples on the level-set of the true function (left-hand-side of Eq. 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(1) is 0, the DA is denoted as label-preserving [Cui et al., 2015,", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 383, + 505, + 395 + ], + "spans": [ + { + "bbox": [ + 106, + 383, + 468, + 395 + ], + "score": 1.0, + "content": "Taylor and Nitschke, 2018]. From the above, we see that unless the target y associated to", + "type": "text" + }, + { + "bbox": [ + 468, + 383, + 494, + 395 + ], + "score": 0.91, + "content": "{ \\mathcal T } _ { \\alpha } ( { \\pmb x } )", + "type": "inline_equation" + }, + { + "bbox": [ + 495, + 383, + 505, + 395 + ], + "score": 1.0, + "content": "is", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 394, + 506, + 407 + ], + "spans": [ + { + "bbox": [ + 105, + 394, + 439, + 407 + ], + "score": 1.0, + "content": "modified accordingly to encode the shift in the target function level-set produced by", + "type": "text" + }, + { + "bbox": [ + 439, + 394, + 451, + 405 + ], + "score": 0.86, + "content": "\\mathcal { T } _ { \\alpha }", + "type": "inline_equation" + }, + { + "bbox": [ + 451, + 394, + 471, + 407 + ], + "score": 1.0, + "content": ", any", + "type": "text" + }, + { + "bbox": [ + 471, + 395, + 486, + 404 + ], + "score": 0.48, + "content": "D A", + "type": "inline_equation" + }, + { + "bbox": [ + 486, + 394, + 506, + 407 + ], + "score": 1.0, + "content": "that", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 405, + 505, + 417 + ], + "spans": [ + { + "bbox": [ + 105, + 405, + 505, + 417 + ], + "score": 1.0, + "content": "is not label-preserving will introduce a bias. Some DAs propose to incorporate label transformation", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 416, + 505, + 428 + ], + "spans": [ + { + "bbox": [ + 105, + 416, + 157, + 428 + ], + "score": 1.0, + "content": "i.e. not only", + "type": "text" + }, + { + "bbox": [ + 158, + 417, + 165, + 426 + ], + "score": 0.73, + "content": "_ { \\textbf { \\em x } }", + "type": "inline_equation" + }, + { + "bbox": [ + 166, + 416, + 200, + 428 + ], + "score": 1.0, + "content": "but also", + "type": "text" + }, + { + "bbox": [ + 200, + 418, + 208, + 428 + ], + "score": 0.77, + "content": "\\textbf { { y } }", + "type": "inline_equation" + }, + { + "bbox": [ + 208, + 416, + 505, + 428 + ], + "score": 1.0, + "content": "is augmented to better inform on the uncertainty that has been added into", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 107, + 426, + 506, + 439 + ], + "spans": [ + { + "bbox": [ + 107, + 426, + 132, + 438 + ], + "score": 0.91, + "content": "\\mathcal { T } _ { \\boldsymbol { \\theta } } ( \\mathbf { \\mathscr { x } } )", + "type": "inline_equation" + }, + { + "bbox": [ + 132, + 426, + 506, + 439 + ], + "score": 1.0, + "content": ". This is for example the case for MixUp [Zhang et al., 2017], ManifoldMixUp [Verma et al.,", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 437, + 325, + 450 + ], + "spans": [ + { + "bbox": [ + 105, + 437, + 325, + 450 + ], + "score": 1.0, + "content": "2019], CutMix [Yun et al., 2019] and their extensions.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 26 + }, + { + "type": "text", + "bbox": [ + 106, + 454, + 505, + 498 + ], + "lines": [ + { + "bbox": [ + 105, + 452, + 507, + 468 + ], + "spans": [ + { + "bbox": [ + 105, + 452, + 507, + 468 + ], + "score": 1.0, + "content": "Our goal in the next Section 3.2 is to demonstrate how DAs such as random crop, color jittering,", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 465, + 506, + 478 + ], + "spans": [ + { + "bbox": [ + 105, + 465, + 338, + 478 + ], + "score": 1.0, + "content": "or CutOut are only label preserving for some values of", + "type": "text" + }, + { + "bbox": [ + 338, + 466, + 346, + 475 + ], + "score": 0.76, + "content": "\\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 346, + 465, + 506, + 478 + ], + "score": 1.0, + "content": "that vary with the sample class. As a", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 476, + 506, + 488 + ], + "spans": [ + { + "bbox": [ + 105, + 476, + 506, + 488 + ], + "score": 1.0, + "content": "consequence, while the use of the DA improves the average test performance, it is at the cost of a", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 487, + 349, + 499 + ], + "spans": [ + { + "bbox": [ + 106, + 487, + 349, + 499 + ], + "score": 1.0, + "content": "significant reduction in performance for some of the classes.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 31.5 + }, + { + "type": "title", + "bbox": [ + 106, + 507, + 504, + 520 + ], + "lines": [ + { + "bbox": [ + 105, + 507, + 505, + 521 + ], + "spans": [ + { + "bbox": [ + 105, + 507, + 505, + 521 + ], + "score": 1.0, + "content": "3.2 The Same Data-Augmentation can be Label-Preserving or Not Between Different Classes", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 34 + }, + { + "type": "text", + "bbox": [ + 107, + 525, + 505, + 591 + ], + "lines": [ + { + "bbox": [ + 105, + 525, + 505, + 537 + ], + "spans": [ + { + "bbox": [ + 105, + 525, + 505, + 537 + ], + "score": 1.0, + "content": "In the previous Section 3.1 we provided a general argument on the sufficient conditions for DA to", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 536, + 506, + 549 + ], + "spans": [ + { + "bbox": [ + 105, + 536, + 506, + 549 + ], + "score": 1.0, + "content": "produce a biased model. We hope in this section to provide a more concrete example that applies", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 546, + 506, + 560 + ], + "spans": [ + { + "bbox": [ + 105, + 546, + 506, + 560 + ], + "score": 1.0, + "content": "to current DNN training. To that end, we will demonstrate that a DA can be label-preserving or not", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 558, + 505, + 570 + ], + "spans": [ + { + "bbox": [ + 105, + 558, + 505, + 570 + ], + "score": 1.0, + "content": "depending on the sample’s class, hence, since the same DA policy is employed for all classes, the", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 104, + 568, + 507, + 582 + ], + "spans": [ + { + "bbox": [ + 104, + 568, + 507, + 582 + ], + "score": 1.0, + "content": "augmented dataset will exhibit a class-imbalance in favor of the classes for which the DA is most", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 578, + 177, + 594 + ], + "spans": [ + { + "bbox": [ + 105, + 578, + 177, + 594 + ], + "score": 1.0, + "content": "label-preserving.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 37.5 + }, + { + "type": "text", + "bbox": [ + 107, + 596, + 505, + 684 + ], + "lines": [ + { + "bbox": [ + 105, + 595, + 507, + 610 + ], + "spans": [ + { + "bbox": [ + 105, + 595, + 259, + 610 + ], + "score": 1.0, + "content": "To measure by how much a given DA,", + "type": "text" + }, + { + "bbox": [ + 259, + 596, + 271, + 607 + ], + "score": 0.86, + "content": "\\mathcal { T } _ { \\alpha }", + "type": "inline_equation" + }, + { + "bbox": [ + 272, + 595, + 507, + 610 + ], + "score": 1.0, + "content": ", is label-preserving, we propose to take 6 popular architec-", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 606, + 506, + 620 + ], + "spans": [ + { + "bbox": [ + 105, + 606, + 506, + 620 + ], + "score": 1.0, + "content": "tures that are pre-trained on Imagenet [Deng et al., 2009] from the official PyTorch [Paszke et al.,", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 617, + 506, + 631 + ], + "spans": [ + { + "bbox": [ + 105, + 617, + 506, + 631 + ], + "score": 1.0, + "content": "2019] repository, and to evaluate their performances for varying DA settings (top of Fig. 5). We", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 628, + 506, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 628, + 506, + 642 + ], + "score": 1.0, + "content": "observe that considering the dataset as a whole is not a good indicator of the optimal DA value to", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 640, + 506, + 653 + ], + "spans": [ + { + "bbox": [ + 105, + 640, + 506, + 653 + ], + "score": 1.0, + "content": "employ since per-class accuracy performance (bottom of Fig. 5) vary drastically. For example, for", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 650, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 106, + 650, + 268, + 663 + ], + "score": 1.0, + "content": "some classes, any level of transformation", + "type": "text" + }, + { + "bbox": [ + 269, + 653, + 276, + 660 + ], + "score": 0.76, + "content": "\\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 277, + 650, + 505, + 663 + ], + "score": 1.0, + "content": "can produce augmented samples with enough information", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 106, + 662, + 505, + 674 + ], + "spans": [ + { + "bbox": [ + 106, + 662, + 505, + 674 + ], + "score": 1.0, + "content": "to be correctly classified, while for other classes, even a small amount of DA makes the samples", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 673, + 165, + 685 + ], + "spans": [ + { + "bbox": [ + 105, + 673, + 165, + 685 + ], + "score": 1.0, + "content": "unpredictable.", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 44.5 + }, + { + "type": "text", + "bbox": [ + 107, + 689, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 689, + 506, + 701 + ], + "spans": [ + { + "bbox": [ + 105, + 689, + 506, + 701 + ], + "score": 1.0, + "content": "To further ensure that the observed relation between label-preservation, sample class, and amount of", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 105, + 700, + 168, + 712 + ], + "score": 1.0, + "content": "transformation", + "type": "text" + }, + { + "bbox": [ + 169, + 702, + 177, + 710 + ], + "score": 0.76, + "content": "\\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 177, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "is sound, we provide in Fig. 6 the per-class test accuracy on different models, all", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 105, + 710, + 505, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 505, + 723 + ], + "score": 1.0, + "content": "exhibit the same trends. In short, we identify that when creating an augmented dataset by applying the", + "type": "text" + } + ], + "index": 51 + } + ], + "index": 50 + } + ], + "page_idx": 5, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 742, + 309, + 750 + ], + "lines": [ + { + "bbox": [ + 302, + 741, + 310, + 752 + ], + "spans": [ + { + "bbox": [ + 302, + 741, + 310, + 752 + ], + "score": 1.0, + "content": "6", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "title", + "bbox": [ + 105, + 72, + 497, + 95 + ], + "lines": [ + { + "bbox": [ + 104, + 71, + 498, + 86 + ], + "spans": [ + { + "bbox": [ + 104, + 71, + 498, + 86 + ], + "score": 1.0, + "content": "3.1 A Data-Augmentation Policy That is Not Label-Preserving For All Classes Will Create", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 129, + 83, + 264, + 96 + ], + "spans": [ + { + "bbox": [ + 129, + 83, + 264, + 96 + ], + "score": 1.0, + "content": "Class-Imbalance Performances", + "type": "text" + } + ], + "index": 1 + } + ], + "index": 0.5 + }, + { + "type": "text", + "bbox": [ + 107, + 103, + 505, + 147 + ], + "lines": [ + { + "bbox": [ + 105, + 102, + 505, + 117 + ], + "spans": [ + { + "bbox": [ + 105, + 102, + 505, + 117 + ], + "score": 1.0, + "content": "To provide a simple explanation on how DA causes bias in a trained model, we propose the following", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 106, + 115, + 505, + 127 + ], + "spans": [ + { + "bbox": [ + 106, + 115, + 505, + 127 + ], + "score": 1.0, + "content": "derivation that holds for any signal e.g. timeseries, images, videos. Without loss of generality, we will", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 125, + 505, + 138 + ], + "spans": [ + { + "bbox": [ + 105, + 125, + 176, + 138 + ], + "score": 1.0, + "content": "consider here the", + "type": "text" + }, + { + "bbox": [ + 176, + 126, + 186, + 136 + ], + "score": 0.87, + "content": "\\ell _ { 2 }", + "type": "inline_equation" + }, + { + "bbox": [ + 186, + 125, + 505, + 138 + ], + "score": 1.0, + "content": "loss which was shown to perform as well as the cross-entropy even on Imagenet", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 136, + 204, + 148 + ], + "spans": [ + { + "bbox": [ + 106, + 136, + 204, + 148 + ], + "score": 1.0, + "content": "[Hui and Belkin, 2020].", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 3.5, + "bbox_fs": [ + 105, + 102, + 505, + 148 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 152, + 506, + 198 + ], + "lines": [ + { + "bbox": [ + 105, + 151, + 505, + 165 + ], + "spans": [ + { + "bbox": [ + 105, + 151, + 257, + 165 + ], + "score": 1.0, + "content": "Dataset notations. Given a sample", + "type": "text" + }, + { + "bbox": [ + 257, + 153, + 290, + 163 + ], + "score": 0.9, + "content": "\\textbf { \\em x } \\in { \\mathcal { X } }", + "type": "inline_equation" + }, + { + "bbox": [ + 290, + 151, + 313, + 165 + ], + "score": 1.0, + "content": "with", + "type": "text" + }, + { + "bbox": [ + 314, + 152, + 355, + 163 + ], + "score": 0.92, + "content": "\\boldsymbol { \\mathcal { X } } \\subset \\mathbb { R } ^ { D }", + "type": "inline_equation" + }, + { + "bbox": [ + 355, + 151, + 414, + 165 + ], + "score": 1.0, + "content": ", we consider", + "type": "text" + }, + { + "bbox": [ + 414, + 152, + 463, + 165 + ], + "score": 0.92, + "content": "{ \\pmb y } \\triangleq f ^ { * } ( { \\pmb x } )", + "type": "inline_equation" + }, + { + "bbox": [ + 463, + 151, + 505, + 165 + ], + "score": 1.0, + "content": "to be the", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 163, + 506, + 177 + ], + "spans": [ + { + "bbox": [ + 105, + 163, + 380, + 177 + ], + "score": 1.0, + "content": "ground-truth target value. Hence our hope is to learn an approximator", + "type": "text" + }, + { + "bbox": [ + 380, + 164, + 390, + 175 + ], + "score": 0.87, + "content": "f _ { \\theta }", + "type": "inline_equation" + }, + { + "bbox": [ + 390, + 163, + 506, + 177 + ], + "score": 1.0, + "content": "that is as close as possible to", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 107, + 174, + 506, + 189 + ], + "spans": [ + { + "bbox": [ + 107, + 176, + 118, + 187 + ], + "score": 0.88, + "content": "f ^ { * }", + "type": "inline_equation" + }, + { + "bbox": [ + 118, + 174, + 178, + 189 + ], + "score": 1.0, + "content": "everywhere in", + "type": "text" + }, + { + "bbox": [ + 179, + 176, + 189, + 186 + ], + "score": 0.78, + "content": "\\mathcal { X }", + "type": "inline_equation" + }, + { + "bbox": [ + 189, + 174, + 375, + 189 + ], + "score": 1.0, + "content": ", although we only observe a finite training set", + "type": "text" + }, + { + "bbox": [ + 376, + 175, + 503, + 187 + ], + "score": 0.89, + "content": "\\mathbb { X } \\triangleq \\{ ( \\pmb { x } _ { 1 } , \\pmb { y } _ { 1 } ) , \\dots , ( \\pmb { x } _ { N } , \\pmb { y } _ { N } ) \\}", + "type": "inline_equation" + }, + { + "bbox": [ + 504, + 174, + 506, + 189 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 186, + 496, + 200 + ], + "spans": [ + { + "bbox": [ + 106, + 186, + 200, + 200 + ], + "score": 1.0, + "content": "Given an output vector", + "type": "text" + }, + { + "bbox": [ + 200, + 189, + 208, + 197 + ], + "score": 0.67, + "content": "\\textbf { \\em u }", + "type": "inline_equation" + }, + { + "bbox": [ + 209, + 186, + 374, + 200 + ], + "score": 1.0, + "content": "we also define the level-set of a mapping", + "type": "text" + }, + { + "bbox": [ + 375, + 187, + 382, + 199 + ], + "score": 0.84, + "content": "f", + "type": "inline_equation" + }, + { + "bbox": [ + 382, + 186, + 406, + 200 + ], + "score": 1.0, + "content": "to be", + "type": "text" + }, + { + "bbox": [ + 406, + 187, + 492, + 198 + ], + "score": 0.88, + "content": "\\{ { \\pmb x } \\in { \\mathcal { X } } : f ( { \\pmb x } ) = { \\pmb u } \\}", + "type": "inline_equation" + }, + { + "bbox": [ + 492, + 186, + 496, + 200 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 7.5, + "bbox_fs": [ + 105, + 151, + 506, + 200 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 198, + 505, + 241 + ], + "lines": [ + { + "bbox": [ + 105, + 196, + 506, + 210 + ], + "spans": [ + { + "bbox": [ + 105, + 196, + 401, + 210 + ], + "score": 1.0, + "content": "Data-Augmentation notations. Additionally, one employs a DA policy", + "type": "text" + }, + { + "bbox": [ + 401, + 198, + 482, + 208 + ], + "score": 0.83, + "content": "\\mathcal { T } : \\mathbb { R } ^ { D } \\times \\mathcal { K } \\mapsto \\mathbb { R } ^ { D }", + "type": "inline_equation" + }, + { + "bbox": [ + 482, + 196, + 506, + 210 + ], + "score": 1.0, + "content": "such", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 208, + 505, + 221 + ], + "spans": [ + { + "bbox": [ + 106, + 208, + 263, + 220 + ], + "score": 1.0, + "content": "that given a transformation parameter", + "type": "text" + }, + { + "bbox": [ + 263, + 209, + 292, + 219 + ], + "score": 0.89, + "content": "\\alpha \\in { \\mathcal { K } }", + "type": "inline_equation" + }, + { + "bbox": [ + 292, + 208, + 296, + 220 + ], + "score": 1.0, + "content": ",", + "type": "text" + }, + { + "bbox": [ + 296, + 208, + 322, + 221 + ], + "score": 0.85, + "content": "{ \\mathcal T } _ { \\alpha } ( { \\pmb x } )", + "type": "inline_equation" + }, + { + "bbox": [ + 323, + 208, + 465, + 220 + ], + "score": 1.0, + "content": "produces the transformed view of", + "type": "text" + }, + { + "bbox": [ + 465, + 210, + 473, + 218 + ], + "score": 0.72, + "content": "_ { \\textbf { \\em x } }", + "type": "inline_equation" + }, + { + "bbox": [ + 473, + 208, + 505, + 220 + ], + "score": 1.0, + "content": ". Often,", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 106, + 219, + 505, + 232 + ], + "spans": [ + { + "bbox": [ + 106, + 219, + 211, + 232 + ], + "score": 1.0, + "content": "one also defines a density", + "type": "text" + }, + { + "bbox": [ + 211, + 221, + 218, + 231 + ], + "score": 0.8, + "content": "p", + "type": "inline_equation" + }, + { + "bbox": [ + 219, + 219, + 231, + 232 + ], + "score": 1.0, + "content": "on", + "type": "text" + }, + { + "bbox": [ + 232, + 220, + 241, + 230 + ], + "score": 0.82, + "content": "\\kappa", + "type": "inline_equation" + }, + { + "bbox": [ + 241, + 219, + 505, + 232 + ], + "score": 1.0, + "content": "that helps in sampling transformation parameters that are a priori", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 231, + 223, + 241 + ], + "spans": [ + { + "bbox": [ + 106, + 231, + 223, + 241 + ], + "score": 1.0, + "content": "known to be the most useful.", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 11.5, + "bbox_fs": [ + 105, + 196, + 506, + 241 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 243, + 505, + 276 + ], + "lines": [ + { + "bbox": [ + 105, + 242, + 506, + 257 + ], + "spans": [ + { + "bbox": [ + 105, + 242, + 328, + 257 + ], + "score": 1.0, + "content": "Theorem 1. Whenever the transformations produced by", + "type": "text" + }, + { + "bbox": [ + 328, + 244, + 357, + 255 + ], + "score": 0.85, + "content": "\\mathcal { T } _ { \\alpha } , \\forall \\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 357, + 242, + 473, + 257 + ], + "score": 1.0, + "content": "do not respect the level-set of", + "type": "text" + }, + { + "bbox": [ + 474, + 244, + 484, + 255 + ], + "score": 0.89, + "content": "f ^ { * }", + "type": "inline_equation" + }, + { + "bbox": [ + 485, + 242, + 506, + 257 + ], + "score": 1.0, + "content": ", and", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 254, + 505, + 267 + ], + "spans": [ + { + "bbox": [ + 105, + 254, + 505, + 267 + ], + "score": 1.0, + "content": "whenever the model has enough capacity to minimize the training loss, the DA will create irreducible", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 264, + 169, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 264, + 135, + 277 + ], + "score": 1.0, + "content": "bias in", + "type": "text" + }, + { + "bbox": [ + 136, + 265, + 146, + 276 + ], + "score": 0.87, + "content": "f _ { \\theta }", + "type": "inline_equation" + }, + { + "bbox": [ + 146, + 264, + 169, + 277 + ], + "score": 1.0, + "content": "as in", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 15, + "bbox_fs": [ + 105, + 242, + 506, + 277 + ] + }, + { + "type": "image", + "bbox": [ + 111, + 280, + 486, + 324 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 111, + 280, + 486, + 324 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 111, + 280, + 486, + 324 + ], + "spans": [ + { + "bbox": [ + 111, + 280, + 486, + 324 + ], + "score": 0.798, + "type": "image", + "image_path": "2ef5e8d9a430737307b40dbef9e6c37e868afc1ea40042ca3717c1fd319a031f.jpg" + } + ] + } + ], + "index": 18, + "virtual_lines": [ + { + "bbox": [ + 111, + 280, + 486, + 294.6666666666667 + ], + "spans": [], + "index": 17 + }, + { + "bbox": [ + 111, + 294.6666666666667, + 486, + 309.33333333333337 + ], + "spans": [], + "index": 18 + }, + { + "bbox": [ + 111, + 309.33333333333337, + 486, + 324.00000000000006 + ], + "spans": [], + "index": 19 + } + ] + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 107, + 333, + 505, + 367 + ], + "lines": [ + { + "bbox": [ + 105, + 334, + 506, + 346 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 314, + 346 + ], + "score": 1.0, + "content": "The main idea of the proof, provided in Appendix", + "type": "text" + }, + { + "bbox": [ + 314, + 334, + 322, + 344 + ], + "score": 0.27, + "content": "\\mathbf { B }", + "type": "inline_equation" + }, + { + "bbox": [ + 322, + 334, + 506, + 346 + ], + "score": 1.0, + "content": ", is to show that if a transformation does not", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 345, + 506, + 357 + ], + "spans": [ + { + "bbox": [ + 105, + 345, + 444, + 357 + ], + "score": 1.0, + "content": "move samples on the level-set of the true function (left-hand-side of Eq. (1)), then", + "type": "text" + }, + { + "bbox": [ + 444, + 345, + 455, + 357 + ], + "score": 0.88, + "content": "f _ { \\theta }", + "type": "inline_equation" + }, + { + "bbox": [ + 455, + 345, + 506, + 357 + ], + "score": 1.0, + "content": "will learn a", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 355, + 463, + 369 + ], + "spans": [ + { + "bbox": [ + 105, + 355, + 334, + 369 + ], + "score": 1.0, + "content": "different level-set (since it has 0 training error), and thus", + "type": "text" + }, + { + "bbox": [ + 334, + 356, + 395, + 368 + ], + "score": 0.93, + "content": "\\| f ^ { * } - f _ { \\theta } \\| > 0", + "type": "inline_equation" + }, + { + "bbox": [ + 396, + 355, + 411, + 369 + ], + "score": 1.0, + "content": "i.e.", + "type": "text" + }, + { + "bbox": [ + 412, + 356, + 422, + 367 + ], + "score": 0.85, + "content": "f _ { \\theta }", + "type": "inline_equation" + }, + { + "bbox": [ + 423, + 355, + 463, + 369 + ], + "score": 1.0, + "content": "is biased.", + "type": "text" + } + ], + "index": 22 + } + ], + "index": 21, + "bbox_fs": [ + 105, + 334, + 506, + 369 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 371, + 505, + 449 + ], + "lines": [ + { + "bbox": [ + 105, + 371, + 506, + 384 + ], + "spans": [ + { + "bbox": [ + 105, + 371, + 506, + 384 + ], + "score": 1.0, + "content": "Whenever the left-hand-side of Eq. (1) is 0, the DA is denoted as label-preserving [Cui et al., 2015,", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 383, + 505, + 395 + ], + "spans": [ + { + "bbox": [ + 106, + 383, + 468, + 395 + ], + "score": 1.0, + "content": "Taylor and Nitschke, 2018]. From the above, we see that unless the target y associated to", + "type": "text" + }, + { + "bbox": [ + 468, + 383, + 494, + 395 + ], + "score": 0.91, + "content": "{ \\mathcal T } _ { \\alpha } ( { \\pmb x } )", + "type": "inline_equation" + }, + { + "bbox": [ + 495, + 383, + 505, + 395 + ], + "score": 1.0, + "content": "is", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 394, + 506, + 407 + ], + "spans": [ + { + "bbox": [ + 105, + 394, + 439, + 407 + ], + "score": 1.0, + "content": "modified accordingly to encode the shift in the target function level-set produced by", + "type": "text" + }, + { + "bbox": [ + 439, + 394, + 451, + 405 + ], + "score": 0.86, + "content": "\\mathcal { T } _ { \\alpha }", + "type": "inline_equation" + }, + { + "bbox": [ + 451, + 394, + 471, + 407 + ], + "score": 1.0, + "content": ", any", + "type": "text" + }, + { + "bbox": [ + 471, + 395, + 486, + 404 + ], + "score": 0.48, + "content": "D A", + "type": "inline_equation" + }, + { + "bbox": [ + 486, + 394, + 506, + 407 + ], + "score": 1.0, + "content": "that", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 405, + 505, + 417 + ], + "spans": [ + { + "bbox": [ + 105, + 405, + 505, + 417 + ], + "score": 1.0, + "content": "is not label-preserving will introduce a bias. Some DAs propose to incorporate label transformation", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 416, + 505, + 428 + ], + "spans": [ + { + "bbox": [ + 105, + 416, + 157, + 428 + ], + "score": 1.0, + "content": "i.e. not only", + "type": "text" + }, + { + "bbox": [ + 158, + 417, + 165, + 426 + ], + "score": 0.73, + "content": "_ { \\textbf { \\em x } }", + "type": "inline_equation" + }, + { + "bbox": [ + 166, + 416, + 200, + 428 + ], + "score": 1.0, + "content": "but also", + "type": "text" + }, + { + "bbox": [ + 200, + 418, + 208, + 428 + ], + "score": 0.77, + "content": "\\textbf { { y } }", + "type": "inline_equation" + }, + { + "bbox": [ + 208, + 416, + 505, + 428 + ], + "score": 1.0, + "content": "is augmented to better inform on the uncertainty that has been added into", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 107, + 426, + 506, + 439 + ], + "spans": [ + { + "bbox": [ + 107, + 426, + 132, + 438 + ], + "score": 0.91, + "content": "\\mathcal { T } _ { \\boldsymbol { \\theta } } ( \\mathbf { \\mathscr { x } } )", + "type": "inline_equation" + }, + { + "bbox": [ + 132, + 426, + 506, + 439 + ], + "score": 1.0, + "content": ". This is for example the case for MixUp [Zhang et al., 2017], ManifoldMixUp [Verma et al.,", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 437, + 325, + 450 + ], + "spans": [ + { + "bbox": [ + 105, + 437, + 325, + 450 + ], + "score": 1.0, + "content": "2019], CutMix [Yun et al., 2019] and their extensions.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 26, + "bbox_fs": [ + 105, + 371, + 506, + 450 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 454, + 505, + 498 + ], + "lines": [ + { + "bbox": [ + 105, + 452, + 507, + 468 + ], + "spans": [ + { + "bbox": [ + 105, + 452, + 507, + 468 + ], + "score": 1.0, + "content": "Our goal in the next Section 3.2 is to demonstrate how DAs such as random crop, color jittering,", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 465, + 506, + 478 + ], + "spans": [ + { + "bbox": [ + 105, + 465, + 338, + 478 + ], + "score": 1.0, + "content": "or CutOut are only label preserving for some values of", + "type": "text" + }, + { + "bbox": [ + 338, + 466, + 346, + 475 + ], + "score": 0.76, + "content": "\\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 346, + 465, + 506, + 478 + ], + "score": 1.0, + "content": "that vary with the sample class. As a", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 476, + 506, + 488 + ], + "spans": [ + { + "bbox": [ + 105, + 476, + 506, + 488 + ], + "score": 1.0, + "content": "consequence, while the use of the DA improves the average test performance, it is at the cost of a", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 487, + 349, + 499 + ], + "spans": [ + { + "bbox": [ + 106, + 487, + 349, + 499 + ], + "score": 1.0, + "content": "significant reduction in performance for some of the classes.", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 31.5, + "bbox_fs": [ + 105, + 452, + 507, + 499 + ] + }, + { + "type": "title", + "bbox": [ + 106, + 507, + 504, + 520 + ], + "lines": [ + { + "bbox": [ + 105, + 507, + 505, + 521 + ], + "spans": [ + { + "bbox": [ + 105, + 507, + 505, + 521 + ], + "score": 1.0, + "content": "3.2 The Same Data-Augmentation can be Label-Preserving or Not Between Different Classes", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 34 + }, + { + "type": "text", + "bbox": [ + 107, + 525, + 505, + 591 + ], + "lines": [ + { + "bbox": [ + 105, + 525, + 505, + 537 + ], + "spans": [ + { + "bbox": [ + 105, + 525, + 505, + 537 + ], + "score": 1.0, + "content": "In the previous Section 3.1 we provided a general argument on the sufficient conditions for DA to", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 536, + 506, + 549 + ], + "spans": [ + { + "bbox": [ + 105, + 536, + 506, + 549 + ], + "score": 1.0, + "content": "produce a biased model. We hope in this section to provide a more concrete example that applies", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 546, + 506, + 560 + ], + "spans": [ + { + "bbox": [ + 105, + 546, + 506, + 560 + ], + "score": 1.0, + "content": "to current DNN training. To that end, we will demonstrate that a DA can be label-preserving or not", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 558, + 505, + 570 + ], + "spans": [ + { + "bbox": [ + 105, + 558, + 505, + 570 + ], + "score": 1.0, + "content": "depending on the sample’s class, hence, since the same DA policy is employed for all classes, the", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 104, + 568, + 507, + 582 + ], + "spans": [ + { + "bbox": [ + 104, + 568, + 507, + 582 + ], + "score": 1.0, + "content": "augmented dataset will exhibit a class-imbalance in favor of the classes for which the DA is most", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 578, + 177, + 594 + ], + "spans": [ + { + "bbox": [ + 105, + 578, + 177, + 594 + ], + "score": 1.0, + "content": "label-preserving.", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 37.5, + "bbox_fs": [ + 104, + 525, + 507, + 594 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 596, + 505, + 684 + ], + "lines": [ + { + "bbox": [ + 105, + 595, + 507, + 610 + ], + "spans": [ + { + "bbox": [ + 105, + 595, + 259, + 610 + ], + "score": 1.0, + "content": "To measure by how much a given DA,", + "type": "text" + }, + { + "bbox": [ + 259, + 596, + 271, + 607 + ], + "score": 0.86, + "content": "\\mathcal { T } _ { \\alpha }", + "type": "inline_equation" + }, + { + "bbox": [ + 272, + 595, + 507, + 610 + ], + "score": 1.0, + "content": ", is label-preserving, we propose to take 6 popular architec-", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 606, + 506, + 620 + ], + "spans": [ + { + "bbox": [ + 105, + 606, + 506, + 620 + ], + "score": 1.0, + "content": "tures that are pre-trained on Imagenet [Deng et al., 2009] from the official PyTorch [Paszke et al.,", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 617, + 506, + 631 + ], + "spans": [ + { + "bbox": [ + 105, + 617, + 506, + 631 + ], + "score": 1.0, + "content": "2019] repository, and to evaluate their performances for varying DA settings (top of Fig. 5). We", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 628, + 506, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 628, + 506, + 642 + ], + "score": 1.0, + "content": "observe that considering the dataset as a whole is not a good indicator of the optimal DA value to", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 640, + 506, + 653 + ], + "spans": [ + { + "bbox": [ + 105, + 640, + 506, + 653 + ], + "score": 1.0, + "content": "employ since per-class accuracy performance (bottom of Fig. 5) vary drastically. For example, for", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 650, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 106, + 650, + 268, + 663 + ], + "score": 1.0, + "content": "some classes, any level of transformation", + "type": "text" + }, + { + "bbox": [ + 269, + 653, + 276, + 660 + ], + "score": 0.76, + "content": "\\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 277, + 650, + 505, + 663 + ], + "score": 1.0, + "content": "can produce augmented samples with enough information", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 106, + 662, + 505, + 674 + ], + "spans": [ + { + "bbox": [ + 106, + 662, + 505, + 674 + ], + "score": 1.0, + "content": "to be correctly classified, while for other classes, even a small amount of DA makes the samples", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 673, + 165, + 685 + ], + "spans": [ + { + "bbox": [ + 105, + 673, + 165, + 685 + ], + "score": 1.0, + "content": "unpredictable.", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 44.5, + "bbox_fs": [ + 105, + 595, + 507, + 685 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 689, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 689, + 506, + 701 + ], + "spans": [ + { + "bbox": [ + 105, + 689, + 506, + 701 + ], + "score": 1.0, + "content": "To further ensure that the observed relation between label-preservation, sample class, and amount of", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 105, + 700, + 168, + 712 + ], + "score": 1.0, + "content": "transformation", + "type": "text" + }, + { + "bbox": [ + 169, + 702, + 177, + 710 + ], + "score": 0.76, + "content": "\\alpha", + "type": "inline_equation" + }, + { + "bbox": [ + 177, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "is sound, we provide in Fig. 6 the per-class test accuracy on different models, all", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 105, + 710, + 505, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 505, + 723 + ], + "score": 1.0, + "content": "exhibit the same trends. In short, we identify that when creating an augmented dataset by applying the", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 105, + 586, + 505, + 599 + ], + "spans": [ + { + "bbox": [ + 105, + 586, + 505, + 599 + ], + "score": 1.0, + "content": "same DA across classes, the number of per-class samples that actually contain enough information", + "type": "text", + "cross_page": true + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 597, + 506, + 610 + ], + "spans": [ + { + "bbox": [ + 105, + 597, + 506, + 610 + ], + "score": 1.0, + "content": "about their true labels will become largely imbalance between classes, even if the original dataset", + "type": "text", + "cross_page": true + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 609, + 505, + 620 + ], + "spans": [ + { + "bbox": [ + 105, + 609, + 505, + 620 + ], + "score": 1.0, + "content": "was balanced. Any model trained on the augmented dataset will thus focus on the classes for which", + "type": "text", + "cross_page": true + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 618, + 253, + 633 + ], + "spans": [ + { + "bbox": [ + 105, + 618, + 253, + 633 + ], + "score": 1.0, + "content": "the DA is the most label-preserving.", + "type": "text", + "cross_page": true + } + ], + "index": 21 + } + ], + "index": 50, + "bbox_fs": [ + 105, + 689, + 506, + 723 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 106, + 69, + 502, + 331 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 106, + 69, + 502, + 331 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 69, + 502, + 331 + ], + "spans": [ + { + "bbox": [ + 106, + 69, + 502, + 331 + ], + "score": 0.971, + "type": "image", + "image_path": "9be1a27a21ea55563825551ac6014351c52449c918b05b84800756d2bc783a05.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 106, + 69, + 502, + 156.33333333333331 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 106, + 156.33333333333331, + 502, + 243.66666666666663 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 106, + 243.66666666666663, + 502, + 330.99999999999994 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 333, + 505, + 413 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 332, + 506, + 345 + ], + "spans": [ + { + "bbox": [ + 105, + 332, + 506, + 345 + ], + "score": 1.0, + "content": "Figure 5: Top: examples of an augmented image of class “bird”. Middle: average accuracy (train set in dashed", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 343, + 505, + 354 + ], + "spans": [ + { + "bbox": [ + 106, + 343, + 505, + 354 + ], + "score": 1.0, + "content": "line and test set in plain) on Imagenet, using 6 popular architectures. Bottom: per-class performances from the", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 352, + 506, + 365 + ], + "spans": [ + { + "bbox": [ + 105, + 352, + 506, + 365 + ], + "score": 1.0, + "content": "middle scenario along with 9 images of the corresponding classes. We observe that the random crop DA seems", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 104, + 362, + 506, + 375 + ], + "spans": [ + { + "bbox": [ + 104, + 362, + 332, + 375 + ], + "score": 1.0, + "content": "to loose its label-preserving property on average when less than", + "type": "text" + }, + { + "bbox": [ + 333, + 363, + 350, + 372 + ], + "score": 0.87, + "content": "30 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 350, + 362, + 506, + 375 + ], + "score": 1.0, + "content": "of the image is kept in the crop but looking", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 373, + 505, + 384 + ], + "spans": [ + { + "bbox": [ + 106, + 373, + 435, + 384 + ], + "score": 1.0, + "content": "at the per-class performance, we observe that such DA can be label-preserving with only", + "type": "text" + }, + { + "bbox": [ + 435, + 373, + 449, + 382 + ], + "score": 0.84, + "content": "8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 450, + 373, + 505, + 384 + ], + "score": 1.0, + "content": "of the original", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 106, + 383, + 505, + 394 + ], + "spans": [ + { + "bbox": [ + 106, + 383, + 453, + 394 + ], + "score": 1.0, + "content": "image for some classes, while for other classes the label information starts to reduce at around", + "type": "text" + }, + { + "bbox": [ + 453, + 383, + 471, + 392 + ], + "score": 0.87, + "content": "50 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 472, + 383, + 505, + 394 + ], + "score": 1.0, + "content": ". Results", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 106, + 392, + 505, + 404 + ], + "spans": [ + { + "bbox": [ + 106, + 392, + 505, + 404 + ], + "score": 1.0, + "content": "obtained from the official Imagenet pre-trained PyTorch models. CutOut and ColorJitter cases are provided in", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 403, + 263, + 414 + ], + "spans": [ + { + "bbox": [ + 106, + 403, + 263, + 414 + ], + "score": 1.0, + "content": "Figs. 14 and 15 and exhibit the same trend.", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 6.5 + } + ], + "index": 3.75 + }, + { + "type": "image", + "bbox": [ + 110, + 418, + 497, + 527 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 110, + 418, + 497, + 527 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 110, + 418, + 497, + 527 + ], + "spans": [ + { + "bbox": [ + 110, + 418, + 497, + 527 + ], + "score": 0.96, + "type": "image", + "image_path": "55ce512e0d33f69dd7ef53b1d5f49f2f68c03d0a122ec2f4392926e06cf02245.jpg" + } + ] + } + ], + "index": 12, + "virtual_lines": [ + { + "bbox": [ + 110, + 418, + 497, + 454.3333333333333 + ], + "spans": [], + "index": 11 + }, + { + "bbox": [ + 110, + 454.3333333333333, + 497, + 490.66666666666663 + ], + "spans": [], + "index": 12 + }, + { + "bbox": [ + 110, + 490.66666666666663, + 497, + 527.0 + ], + "spans": [], + "index": 13 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 107, + 528, + 505, + 569 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 106, + 527, + 505, + 540 + ], + "spans": [ + { + "bbox": [ + 106, + 527, + 505, + 540 + ], + "score": 1.0, + "content": "Figure 6: Reprise of the bottom left of Fig. 5 for three different DAs (each column) and using the same", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 538, + 505, + 550 + ], + "spans": [ + { + "bbox": [ + 105, + 538, + 505, + 550 + ], + "score": 1.0, + "content": "6 popular architectures (different lines). We observe that across DAs, different architectures agree on the", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 548, + 505, + 560 + ], + "spans": [ + { + "bbox": [ + 105, + 548, + 208, + 560 + ], + "score": 1.0, + "content": "label-preserving regimes for", + "type": "text" + }, + { + "bbox": [ + 208, + 548, + 219, + 558 + ], + "score": 0.88, + "content": "\\mathcal { T } _ { \\alpha }", + "type": "inline_equation" + }, + { + "bbox": [ + 220, + 548, + 505, + 560 + ], + "score": 1.0, + "content": "i.e. even an ensemble of model would not reduce the class-dependent bias of the", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 558, + 428, + 570 + ], + "spans": [ + { + "bbox": [ + 106, + 558, + 428, + 570 + ], + "score": 1.0, + "content": "final prediction. Results obtained from the official Imagenet pre-trained PyTorch models.", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 15.5 + } + ], + "index": 13.75 + }, + { + "type": "text", + "bbox": [ + 107, + 586, + 505, + 631 + ], + "lines": [ + { + "bbox": [ + 105, + 586, + 505, + 599 + ], + "spans": [ + { + "bbox": [ + 105, + 586, + 505, + 599 + ], + "score": 1.0, + "content": "same DA across classes, the number of per-class samples that actually contain enough information", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 597, + 506, + 610 + ], + "spans": [ + { + "bbox": [ + 105, + 597, + 506, + 610 + ], + "score": 1.0, + "content": "about their true labels will become largely imbalance between classes, even if the original dataset", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 609, + 505, + 620 + ], + "spans": [ + { + "bbox": [ + 105, + 609, + 505, + 620 + ], + "score": 1.0, + "content": "was balanced. Any model trained on the augmented dataset will thus focus on the classes for which", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 618, + 253, + 633 + ], + "spans": [ + { + "bbox": [ + 105, + 618, + 253, + 633 + ], + "score": 1.0, + "content": "the DA is the most label-preserving.", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 19.5 + }, + { + "type": "text", + "bbox": [ + 106, + 635, + 504, + 658 + ], + "lines": [ + { + "bbox": [ + 105, + 635, + 505, + 648 + ], + "spans": [ + { + "bbox": [ + 105, + 635, + 505, + 648 + ], + "score": 1.0, + "content": "Beyond the above intuitive and natural understand we obtained in term of data-augmentation, there", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 647, + 354, + 658 + ], + "spans": [ + { + "bbox": [ + 106, + 647, + 354, + 658 + ], + "score": 1.0, + "content": "exists theoretical studies that we propose to summarize below", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 22.5 + }, + { + "type": "title", + "bbox": [ + 105, + 667, + 493, + 689 + ], + "lines": [ + { + "bbox": [ + 106, + 666, + 493, + 680 + ], + "spans": [ + { + "bbox": [ + 106, + 666, + 493, + 680 + ], + "score": 1.0, + "content": "3.3 Do Existing Studies Provide Answers Into the Inter-Play Between Regularization and", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 128, + 677, + 229, + 690 + ], + "spans": [ + { + "bbox": [ + 128, + 677, + 229, + 690 + ], + "score": 1.0, + "content": "Class-Dependent Bias?", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 24.5 + }, + { + "type": "text", + "bbox": [ + 107, + 699, + 503, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 699, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 699, + 505, + 712 + ], + "score": 1.0, + "content": "We explored in Section 3.2 a possible explanation of the class-dependent bias we observed in Section 2", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 711, + 505, + 723 + ], + "spans": [ + { + "bbox": [ + 106, + 711, + 505, + 723 + ], + "score": 1.0, + "content": "hinting at the need to use class-dependent DA. 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In this work, it was theorized that when the underlying", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 106, + 318, + 506, + 330 + ], + "spans": [ + { + "bbox": [ + 106, + 318, + 506, + 330 + ], + "score": 1.0, + "content": "dataset contains inherent biases, training on the original data is more effective than employing an i.i.d.", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 328, + 506, + 342 + ], + "spans": [ + { + "bbox": [ + 105, + 328, + 506, + 342 + ], + "score": 1.0, + "content": "DA policy, i.e. applying the same random augmentation to all samples/classes, to produce an unbiased", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 339, + 506, + 352 + ], + "spans": [ + { + "bbox": [ + 106, + 339, + 506, + 352 + ], + "score": 1.0, + "content": "model. In short, the DA exacerbates the already present biases and makes the trained model further", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 351, + 506, + 363 + ], + "spans": [ + { + "bbox": [ + 105, + 351, + 506, + 363 + ], + "score": 1.0, + "content": "away from the unbiased optimum. The difficulty of this result lies in defining bias for real images.", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 361, + 505, + 375 + ], + "spans": [ + { + "bbox": [ + 105, + 361, + 505, + 375 + ], + "score": 1.0, + "content": "As per our experiments from Section 3, we observe that bias can take many form e.g. one class might", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 373, + 505, + 385 + ], + "spans": [ + { + "bbox": [ + 106, + 373, + 505, + 385 + ], + "score": 1.0, + "content": "naturally represent its object always under the same angle. This is particularly true say for boats", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 384, + 506, + 396 + ], + "spans": [ + { + "bbox": [ + 106, + 384, + 506, + 396 + ], + "score": 1.0, + "content": "which are rarely captured upside-down. Hence, simply having classes with different natural statistics", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 104, + 393, + 507, + 408 + ], + "spans": [ + { + "bbox": [ + 104, + 393, + 507, + 408 + ], + "score": 1.0, + "content": "could be enough for Xu et al. [2020] to prohibit the use of DA in current datasets. Furthermore,", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 405, + 506, + 418 + ], + "spans": [ + { + "bbox": [ + 106, + 405, + 506, + 418 + ], + "score": 1.0, + "content": "Raghunathan et al. [2020] obtained a surprising result combining both DA and regularization. In", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 416, + 506, + 429 + ], + "spans": [ + { + "bbox": [ + 105, + 416, + 397, + 429 + ], + "score": 1.0, + "content": "that case, it was found that the minimum norm interpolant on the original", + "type": "text" + }, + { + "bbox": [ + 398, + 417, + 422, + 427 + ], + "score": 0.28, + "content": "+ \\textrm Ḋ \\textmu Ḋ Ḍ Ḍ _ { \\mathrm { Ḋ } } \\textrm Ḋ \\textmu Ḍ Ḍ", + "type": "inline_equation" + }, + { + "bbox": [ + 423, + 416, + 506, + 429 + ], + "score": 1.0, + "content": "dataset could have a", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 427, + 505, + 439 + ], + "spans": [ + { + "bbox": [ + 105, + 427, + 505, + 439 + ], + "score": 1.0, + "content": "larger standard error than the minimum norm interpolant on the original dataset alone, even when", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 438, + 506, + 450 + ], + "spans": [ + { + "bbox": [ + 106, + 438, + 506, + 450 + ], + "score": 1.0, + "content": "using label-preserving DA. However, this phenomenon only occurs as long as the model remains", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 449, + 378, + 461 + ], + "spans": [ + { + "bbox": [ + 106, + 450, + 324, + 461 + ], + "score": 1.0, + "content": "over-parametrized, even when considering the original", + "type": "text" + }, + { + "bbox": [ + 324, + 449, + 345, + 460 + ], + "score": 0.41, + "content": "+ \\mathrm { D A }", + "type": "inline_equation" + }, + { + "bbox": [ + 345, + 450, + 378, + 461 + ], + "score": 1.0, + "content": "dataset.", + "type": "text" + } + ], + "index": 42 + } + ], + "index": 35 + }, + { + "type": "text", + "bbox": [ + 106, + 460, + 505, + 547 + ], + "lines": [ + { + "bbox": [ + 106, + 460, + 506, + 473 + ], + "spans": [ + { + "bbox": [ + 106, + 460, + 506, + 473 + ], + "score": 1.0, + "content": "Input dependent DA can reduce sample/class bias. Recall that Section 3.2 brought forward one", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 470, + 506, + 484 + ], + "spans": [ + { + "bbox": [ + 105, + 470, + 506, + 484 + ], + "score": 1.0, + "content": "possible explanation on how DA can be the source of training-set by introduce class-imbalance due to", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 482, + 505, + 494 + ], + "spans": [ + { + "bbox": [ + 106, + 482, + 505, + 494 + ], + "score": 1.0, + "content": "the same DA being label-preserving for some classes and not for others. From this, a direct solution", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 491, + 506, + 506 + ], + "spans": [ + { + "bbox": [ + 105, + 491, + 506, + 506 + ], + "score": 1.0, + "content": "would be to adapt the “strength”. This solution, formalized in Xu et al. [2020], consists in measuring", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 503, + 506, + 516 + ], + "spans": [ + { + "bbox": [ + 105, + 503, + 506, + 516 + ], + "score": 1.0, + "content": "the bias of a model and adapt the DA policy accordingly to correct it. This has been done in different", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 106, + 514, + 506, + 528 + ], + "spans": [ + { + "bbox": [ + 106, + 514, + 506, + 528 + ], + "score": 1.0, + "content": "flavors e.g. in McLaughlin et al. [2015], Iosifidis and Ntoutsi [2018], Jaipuria et al. [2020]. One", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 104, + 523, + 505, + 540 + ], + "spans": [ + { + "bbox": [ + 104, + 523, + 505, + 540 + ], + "score": 1.0, + "content": "limitation of this direction is that it requires to estimate a model bias and adapt the DA accordingly", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 536, + 303, + 549 + ], + "spans": [ + { + "bbox": [ + 105, + 536, + 303, + 549 + ], + "score": 1.0, + "content": "which can be challenging for large scale models.", + "type": "text" + } + ], + "index": 50 + } + ], + "index": 46.5 + }, + { + "type": "text", + "bbox": [ + 106, + 547, + 505, + 635 + ], + "lines": [ + { + "bbox": [ + 105, + 546, + 506, + 560 + ], + "spans": [ + { + "bbox": [ + 105, + 546, + 506, + 560 + ], + "score": 1.0, + "content": "Learned DA e.g. from a GAN can produce even more class-dependent bias. One natural extension", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 106, + 558, + 506, + 570 + ], + "spans": [ + { + "bbox": [ + 106, + 558, + 506, + 570 + ], + "score": 1.0, + "content": "of the hands-on adaptivity of a DA to a measured model bias would be to learn a DA to maximize a", + "type": "text" + } + ], + "index": 52 + }, + { + "bbox": [ + 105, + 567, + 505, + 583 + ], + "spans": [ + { + "bbox": [ + 105, + 567, + 505, + 583 + ], + "score": 1.0, + "content": "model’s performance, for example. This line of work has led to many learn DA policies e.g. using", + "type": "text" + } + ], + "index": 53 + }, + { + "bbox": [ + 106, + 580, + 505, + 592 + ], + "spans": [ + { + "bbox": [ + 106, + 580, + 505, + 592 + ], + "score": 1.0, + "content": "Generative Adversarial Networks [Hu and Li, 2019]. Yet, it has been shown that learning a DA to", + "type": "text" + } + ], + "index": 54 + }, + { + "bbox": [ + 105, + 591, + 505, + 603 + ], + "spans": [ + { + "bbox": [ + 105, + 591, + 505, + 603 + ], + "score": 1.0, + "content": "maximize some aggregated measure of performance will produce even more bias in a model [Hu", + "type": "text" + } + ], + "index": 55 + }, + { + "bbox": [ + 106, + 601, + 505, + 614 + ], + "spans": [ + { + "bbox": [ + 106, + 601, + 505, + 614 + ], + "score": 1.0, + "content": "and Li, 2019]. In fact, and as per the controlled experiments from Section 2, the learned DA will", + "type": "text" + } + ], + "index": 56 + }, + { + "bbox": [ + 106, + 612, + 506, + 626 + ], + "spans": [ + { + "bbox": [ + 106, + 612, + 506, + 626 + ], + "score": 1.0, + "content": "entirely disregard a minority of the classes if it means that the produced DA can drastically improve", + "type": "text" + } + ], + "index": 57 + }, + { + "bbox": [ + 106, + 624, + 419, + 636 + ], + "spans": [ + { + "bbox": [ + 106, + 624, + 419, + 636 + ], + "score": 1.0, + "content": "performances on all others, effectively maximizing the average performances.", + "type": "text" + } + ], + "index": 58 + } + ], + "index": 54.5 + }, + { + "type": "text", + "bbox": [ + 107, + 635, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 634, + 505, + 647 + ], + "spans": [ + { + "bbox": [ + 106, + 634, + 505, + 647 + ], + "score": 1.0, + "content": "Inherent tradeoff between DA and model robustness. In addition to the implication of DA", + "type": "text" + } + ], + "index": 59 + }, + { + "bbox": [ + 106, + 645, + 506, + 657 + ], + "spans": [ + { + "bbox": [ + 106, + 645, + 506, + 657 + ], + "score": 1.0, + "content": "into bias, there exists an intertwined relationship between DA and model robustness. In fact, even", + "type": "text" + } + ], + "index": 60 + }, + { + "bbox": [ + 106, + 657, + 506, + 668 + ], + "spans": [ + { + "bbox": [ + 106, + 657, + 506, + 668 + ], + "score": 1.0, + "content": "assuming the use of perfectly adapted DAs, there exists an inherent tradeoff between accuracy and", + "type": "text" + } + ], + "index": 61 + }, + { + "bbox": [ + 106, + 667, + 506, + 680 + ], + "spans": [ + { + "bbox": [ + 106, + 667, + 506, + 680 + ], + "score": 1.0, + "content": "robustness that holds even in the infinite data limit [Tsipras et al., 2018, Fawzi et al., 2018, Zhang et al.,", + "type": "text" + } + ], + "index": 62 + }, + { + "bbox": [ + 105, + 677, + 506, + 692 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 506, + 692 + ], + "score": 1.0, + "content": "2019]. For example, Min et al. [2021] proved in the robust linear classification regime that (i) more", + "type": "text" + } + ], + "index": 63 + }, + { + "bbox": [ + 105, + 688, + 506, + 702 + ], + "spans": [ + { + "bbox": [ + 105, + 688, + 506, + 702 + ], + "score": 1.0, + "content": "data improves generalization in a weak adversary regime, (ii) more data can improve generalization", + "type": "text" + } + ], + "index": 64 + }, + { + "bbox": [ + 105, + 699, + 506, + 714 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 506, + 714 + ], + "score": 1.0, + "content": "up to a point where additional data starts to hurt generalization in a medium adversary regime, and", + "type": "text" + } + ], + "index": 65 + }, + { + "bbox": [ + 105, + 710, + 474, + 724 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 474, + 724 + ], + "score": 1.0, + "content": "that (iii) more data immediately decreases generalization error in a strong adversary regime.", + "type": "text" + } + ], + "index": 66 + } + ], + "index": 62.5 + } + ], + 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+ 326, + 76, + 506, + 89 + ], + "spans": [ + { + "bbox": [ + 326, + 76, + 506, + 89 + ], + "score": 1.0, + "content": "Figure 7: Reprise of Fig. 2 but now implement-", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 326, + 87, + 506, + 99 + ], + "spans": [ + { + "bbox": [ + 326, + 87, + 506, + 99 + ], + "score": 1.0, + "content": "ing the class-dependent DA as prescribed in Sec-", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 326, + 96, + 505, + 109 + ], + "spans": [ + { + "bbox": [ + 326, + 96, + 505, + 109 + ], + "score": 1.0, + "content": "tion 3.3 i.e. we only apply DA (random crop) to", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 326, + 107, + 505, + 118 + ], + "spans": [ + { + "bbox": [ + 326, + 107, + 505, + 118 + ], + "score": 1.0, + "content": "the classes that see their per-class accuracy im-", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 326, + 117, + 506, + 129 + ], + "spans": [ + { + "bbox": [ + 326, + 117, + 506, + 129 + ], + "score": 1.0, + "content": "prove when this DA is employed during training..", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 326, + 127, + 505, + 138 + ], + "spans": [ + { + "bbox": [ + 326, + 127, + 505, + 138 + ], + "score": 1.0, + "content": "We observe here that the bias of the model intro-", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 327, + 137, + 504, + 148 + ], + "spans": [ + { + "bbox": [ + 327, + 137, + 504, + 148 + ], + "score": 1.0, + "content": "duced from random crop on the majority of the", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 327, + 147, + 505, + 158 + ], + "spans": [ + { + "bbox": [ + 327, + 147, + 505, + 158 + ], + "score": 1.0, + "content": "classes spills-over to the minority of the classes", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 326, + 156, + 505, + 168 + ], + "spans": [ + { + "bbox": [ + 326, + 156, + 505, + 168 + ], + "score": 1.0, + "content": "that do not benefit from that DA, even though", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 326, + 167, + 505, + 178 + ], + "spans": [ + { + "bbox": [ + 326, + 167, + 505, + 178 + ], + "score": 1.0, + "content": "such classes never received that DA during train-", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 326, + 177, + 504, + 188 + ], + "spans": [ + { + "bbox": [ + 326, + 177, + 504, + 188 + ], + "score": 1.0, + "content": "ing. Results are averaged over 5 runs and employ", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 326, + 187, + 504, + 198 + ], + "spans": [ + { + "bbox": [ + 326, + 187, + 504, + 198 + ], + "score": 1.0, + "content": "the official resnet50 implementation trained on", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 326, + 196, + 506, + 208 + ], + "spans": [ + { + "bbox": [ + 326, + 196, + 506, + 208 + ], + "score": 1.0, + "content": "Imagenet with horizontal flip but a per-class ran-", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 326, + 207, + 381, + 218 + ], + "spans": [ + { + "bbox": [ + 326, + 207, + 381, + 218 + ], + "score": 1.0, + "content": "dom crop DA.", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 18.5 + } + ], + "index": 12.0 + }, + { + "type": "text", + "bbox": [ + 105, + 269, + 504, + 291 + ], + "lines": [], + "index": 26.5, + "bbox_fs": [ + 105, + 268, + 505, + 292 + ], + "lines_deleted": true + }, + { + "type": "text", + "bbox": [ + 106, + 296, + 505, + 460 + ], + "lines": [ + { + "bbox": [ + 105, + 296, + 506, + 308 + ], + "spans": [ + { + "bbox": [ + 105, + 296, + 506, + 308 + ], + "score": 1.0, + "content": "Input independent DA exacerbates the bias already present in a dataset. Especially relevant to", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 104, + 304, + 506, + 322 + ], + "spans": [ + { + "bbox": [ + 104, + 304, + 506, + 322 + ], + "score": 1.0, + "content": "our results is a recent result of Xu et al. [2020]. In this work, it was theorized that when the underlying", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 106, + 318, + 506, + 330 + ], + "spans": [ + { + "bbox": [ + 106, + 318, + 506, + 330 + ], + "score": 1.0, + "content": "dataset contains inherent biases, training on the original data is more effective than employing an i.i.d.", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 328, + 506, + 342 + ], + "spans": [ + { + "bbox": [ + 105, + 328, + 506, + 342 + ], + "score": 1.0, + "content": "DA policy, i.e. applying the same random augmentation to all samples/classes, to produce an unbiased", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 106, + 339, + 506, + 352 + ], + "spans": [ + { + "bbox": [ + 106, + 339, + 506, + 352 + ], + "score": 1.0, + "content": "model. In short, the DA exacerbates the already present biases and makes the trained model further", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 351, + 506, + 363 + ], + "spans": [ + { + "bbox": [ + 105, + 351, + 506, + 363 + ], + "score": 1.0, + "content": "away from the unbiased optimum. The difficulty of this result lies in defining bias for real images.", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 361, + 505, + 375 + ], + "spans": [ + { + "bbox": [ + 105, + 361, + 505, + 375 + ], + "score": 1.0, + "content": "As per our experiments from Section 3, we observe that bias can take many form e.g. one class might", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 106, + 373, + 505, + 385 + ], + "spans": [ + { + "bbox": [ + 106, + 373, + 505, + 385 + ], + "score": 1.0, + "content": "naturally represent its object always under the same angle. This is particularly true say for boats", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 384, + 506, + 396 + ], + "spans": [ + { + "bbox": [ + 106, + 384, + 506, + 396 + ], + "score": 1.0, + "content": "which are rarely captured upside-down. Hence, simply having classes with different natural statistics", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 104, + 393, + 507, + 408 + ], + "spans": [ + { + "bbox": [ + 104, + 393, + 507, + 408 + ], + "score": 1.0, + "content": "could be enough for Xu et al. [2020] to prohibit the use of DA in current datasets. Furthermore,", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 405, + 506, + 418 + ], + "spans": [ + { + "bbox": [ + 106, + 405, + 506, + 418 + ], + "score": 1.0, + "content": "Raghunathan et al. [2020] obtained a surprising result combining both DA and regularization. In", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 416, + 506, + 429 + ], + "spans": [ + { + "bbox": [ + 105, + 416, + 397, + 429 + ], + "score": 1.0, + "content": "that case, it was found that the minimum norm interpolant on the original", + "type": "text" + }, + { + "bbox": [ + 398, + 417, + 422, + 427 + ], + "score": 0.28, + "content": "+ \\textrm Ḋ \\textmu Ḋ Ḍ Ḍ _ { \\mathrm { Ḋ } } \\textrm Ḋ \\textmu Ḍ Ḍ", + "type": "inline_equation" + }, + { + "bbox": [ + 423, + 416, + 506, + 429 + ], + "score": 1.0, + "content": "dataset could have a", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 427, + 505, + 439 + ], + "spans": [ + { + "bbox": [ + 105, + 427, + 505, + 439 + ], + "score": 1.0, + "content": "larger standard error than the minimum norm interpolant on the original dataset alone, even when", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 106, + 438, + 506, + 450 + ], + "spans": [ + { + "bbox": [ + 106, + 438, + 506, + 450 + ], + "score": 1.0, + "content": "using label-preserving DA. However, this phenomenon only occurs as long as the model remains", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 449, + 378, + 461 + ], + "spans": [ + { + "bbox": [ + 106, + 450, + 324, + 461 + ], + "score": 1.0, + "content": "over-parametrized, even when considering the original", + "type": "text" + }, + { + "bbox": [ + 324, + 449, + 345, + 460 + ], + "score": 0.41, + "content": "+ \\mathrm { D A }", + "type": "inline_equation" + }, + { + "bbox": [ + 345, + 450, + 378, + 461 + ], + "score": 1.0, + "content": "dataset.", + "type": "text" + } + ], + "index": 42 + } + ], + "index": 35, + "bbox_fs": [ + 104, + 296, + 507, + 461 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 460, + 505, + 547 + ], + "lines": [ + { + "bbox": [ + 106, + 460, + 506, + 473 + ], + "spans": [ + { + "bbox": [ + 106, + 460, + 506, + 473 + ], + "score": 1.0, + "content": "Input dependent DA can reduce sample/class bias. Recall that Section 3.2 brought forward one", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 470, + 506, + 484 + ], + "spans": [ + { + "bbox": [ + 105, + 470, + 506, + 484 + ], + "score": 1.0, + "content": "possible explanation on how DA can be the source of training-set by introduce class-imbalance due to", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 482, + 505, + 494 + ], + "spans": [ + { + "bbox": [ + 106, + 482, + 505, + 494 + ], + "score": 1.0, + "content": "the same DA being label-preserving for some classes and not for others. From this, a direct solution", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 491, + 506, + 506 + ], + "spans": [ + { + "bbox": [ + 105, + 491, + 506, + 506 + ], + "score": 1.0, + "content": "would be to adapt the “strength”. This solution, formalized in Xu et al. [2020], consists in measuring", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 503, + 506, + 516 + ], + "spans": [ + { + "bbox": [ + 105, + 503, + 506, + 516 + ], + "score": 1.0, + "content": "the bias of a model and adapt the DA policy accordingly to correct it. This has been done in different", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 106, + 514, + 506, + 528 + ], + "spans": [ + { + "bbox": [ + 106, + 514, + 506, + 528 + ], + "score": 1.0, + "content": "flavors e.g. in McLaughlin et al. [2015], Iosifidis and Ntoutsi [2018], Jaipuria et al. [2020]. One", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 104, + 523, + 505, + 540 + ], + "spans": [ + { + "bbox": [ + 104, + 523, + 505, + 540 + ], + "score": 1.0, + "content": "limitation of this direction is that it requires to estimate a model bias and adapt the DA accordingly", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 105, + 536, + 303, + 549 + ], + "spans": [ + { + "bbox": [ + 105, + 536, + 303, + 549 + ], + "score": 1.0, + "content": "which can be challenging for large scale models.", + "type": "text" + } + ], + "index": 50 + } + ], + "index": 46.5, + "bbox_fs": [ + 104, + 460, + 506, + 549 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 547, + 505, + 635 + ], + "lines": [ + { + "bbox": [ + 105, + 546, + 506, + 560 + ], + "spans": [ + { + "bbox": [ + 105, + 546, + 506, + 560 + ], + "score": 1.0, + "content": "Learned DA e.g. from a GAN can produce even more class-dependent bias. One natural extension", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 106, + 558, + 506, + 570 + ], + "spans": [ + { + "bbox": [ + 106, + 558, + 506, + 570 + ], + "score": 1.0, + "content": "of the hands-on adaptivity of a DA to a measured model bias would be to learn a DA to maximize a", + "type": "text" + } + ], + "index": 52 + }, + { + "bbox": [ + 105, + 567, + 505, + 583 + ], + "spans": [ + { + "bbox": [ + 105, + 567, + 505, + 583 + ], + "score": 1.0, + "content": "model’s performance, for example. This line of work has led to many learn DA policies e.g. using", + "type": "text" + } + ], + "index": 53 + }, + { + "bbox": [ + 106, + 580, + 505, + 592 + ], + "spans": [ + { + "bbox": [ + 106, + 580, + 505, + 592 + ], + "score": 1.0, + "content": "Generative Adversarial Networks [Hu and Li, 2019]. Yet, it has been shown that learning a DA to", + "type": "text" + } + ], + "index": 54 + }, + { + "bbox": [ + 105, + 591, + 505, + 603 + ], + "spans": [ + { + "bbox": [ + 105, + 591, + 505, + 603 + ], + "score": 1.0, + "content": "maximize some aggregated measure of performance will produce even more bias in a model [Hu", + "type": "text" + } + ], + "index": 55 + }, + { + "bbox": [ + 106, + 601, + 505, + 614 + ], + "spans": [ + { + "bbox": [ + 106, + 601, + 505, + 614 + ], + "score": 1.0, + "content": "and Li, 2019]. In fact, and as per the controlled experiments from Section 2, the learned DA will", + "type": "text" + } + ], + "index": 56 + }, + { + "bbox": [ + 106, + 612, + 506, + 626 + ], + "spans": [ + { + "bbox": [ + 106, + 612, + 506, + 626 + ], + "score": 1.0, + "content": "entirely disregard a minority of the classes if it means that the produced DA can drastically improve", + "type": "text" + } + ], + "index": 57 + }, + { + "bbox": [ + 106, + 624, + 419, + 636 + ], + "spans": [ + { + "bbox": [ + 106, + 624, + 419, + 636 + ], + "score": 1.0, + "content": "performances on all others, effectively maximizing the average performances.", + "type": "text" + } + ], + "index": 58 + } + ], + "index": 54.5, + "bbox_fs": [ + 105, + 546, + 506, + 636 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 635, + 505, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 634, + 505, + 647 + ], + "spans": [ + { + "bbox": [ + 106, + 634, + 505, + 647 + ], + "score": 1.0, + "content": "Inherent tradeoff between DA and model robustness. In addition to the implication of DA", + "type": "text" + } + ], + "index": 59 + }, + { + "bbox": [ + 106, + 645, + 506, + 657 + ], + "spans": [ + { + "bbox": [ + 106, + 645, + 506, + 657 + ], + "score": 1.0, + "content": "into bias, there exists an intertwined relationship between DA and model robustness. In fact, even", + "type": "text" + } + ], + "index": 60 + }, + { + "bbox": [ + 106, + 657, + 506, + 668 + ], + "spans": [ + { + "bbox": [ + 106, + 657, + 506, + 668 + ], + "score": 1.0, + "content": "assuming the use of perfectly adapted DAs, there exists an inherent tradeoff between accuracy and", + "type": "text" + } + ], + "index": 61 + }, + { + "bbox": [ + 106, + 667, + 506, + 680 + ], + "spans": [ + { + "bbox": [ + 106, + 667, + 506, + 680 + ], + "score": 1.0, + "content": "robustness that holds even in the infinite data limit [Tsipras et al., 2018, Fawzi et al., 2018, Zhang et al.,", + "type": "text" + } + ], + "index": 62 + }, + { + "bbox": [ + 105, + 677, + 506, + 692 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 506, + 692 + ], + "score": 1.0, + "content": "2019]. For example, Min et al. [2021] proved in the robust linear classification regime that (i) more", + "type": "text" + } + ], + "index": 63 + }, + { + "bbox": [ + 105, + 688, + 506, + 702 + ], + "spans": [ + { + "bbox": [ + 105, + 688, + 506, + 702 + ], + "score": 1.0, + "content": "data improves generalization in a weak adversary regime, (ii) more data can improve generalization", + "type": "text" + } + ], + "index": 64 + }, + { + "bbox": [ + 105, + 699, + 506, + 714 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 506, + 714 + ], + "score": 1.0, + "content": "up to a point where additional data starts to hurt generalization in a medium adversary regime, and", + "type": "text" + } + ], + "index": 65 + }, + { + "bbox": [ + 105, + 710, + 474, + 724 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 474, + 724 + ], + "score": 1.0, + "content": "that (iii) more data immediately decreases generalization error in a strong adversary regime.", + "type": "text" + } + ], + "index": 66 + } + ], + "index": 62.5, + "bbox_fs": [ + 105, + 634, + 506, + 724 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "title", + "bbox": [ + 105, + 72, + 483, + 84 + ], + "lines": [ + { + "bbox": [ + 104, + 69, + 484, + 88 + ], + "spans": [ + { + "bbox": [ + 104, + 69, + 484, + 88 + ], + "score": 1.0, + "content": "3.4 Class-Dependent Data-Augmentation Seems Insufficient for Performance Recovery", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "text", + "bbox": [ + 107, + 97, + 505, + 153 + ], + "lines": [ + { + "bbox": [ + 106, + 97, + 506, + 111 + ], + "spans": [ + { + "bbox": [ + 106, + 97, + 506, + 111 + ], + "score": 1.0, + "content": "We observed in Section 2 that DA could lead to disastrous per-class performances on a minority of", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 108, + 505, + 122 + ], + "spans": [ + { + "bbox": [ + 105, + 108, + 505, + 122 + ], + "score": 1.0, + "content": "classes. We now propose to implement one solution from Section 3.3 that consists in simply not", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 119, + 506, + 133 + ], + "spans": [ + { + "bbox": [ + 105, + 119, + 506, + 133 + ], + "score": 1.0, + "content": "applying the harmful DA to the classes suffering from it. As will become clear, applying the DA on", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 129, + 505, + 145 + ], + "spans": [ + { + "bbox": [ + 105, + 129, + 505, + 145 + ], + "score": 1.0, + "content": "all other classes will be enough to skew the training of the model preventing any performing recovery", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 141, + 176, + 154 + ], + "spans": [ + { + "bbox": [ + 105, + 141, + 176, + 154 + ], + "score": 1.0, + "content": "on those classes.", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 3 + }, + { + "type": "text", + "bbox": [ + 107, + 158, + 505, + 420 + ], + "lines": [ + { + "bbox": [ + 105, + 158, + 505, + 171 + ], + "spans": [ + { + "bbox": [ + 105, + 158, + 505, + 171 + ], + "score": 1.0, + "content": "To motivate the need for a better control of the per-class performance of a model, we first present an", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 167, + 505, + 183 + ], + "spans": [ + { + "bbox": [ + 105, + 167, + 365, + 183 + ], + "score": 1.0, + "content": "illustrative argument. A standard resnet50 on Imagenet reaches", + "type": "text" + }, + { + "bbox": [ + 366, + 169, + 398, + 180 + ], + "score": 0.89, + "content": "7 7 . 1 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 398, + 167, + 505, + 183 + ], + "score": 1.0, + "content": "top-1 with a random crop", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 178, + 505, + 194 + ], + "spans": [ + { + "bbox": [ + 105, + 178, + 170, + 194 + ], + "score": 1.0, + "content": "lower bound of", + "type": "text" + }, + { + "bbox": [ + 170, + 180, + 185, + 190 + ], + "score": 0.87, + "content": "8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 185, + 178, + 505, + 194 + ], + "score": 1.0, + "content": ". Using precise cross-validation, one could reach 77.29 by using a random crop", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 189, + 506, + 205 + ], + "spans": [ + { + "bbox": [ + 105, + 189, + 169, + 205 + ], + "score": 1.0, + "content": "lower bound of", + "type": "text" + }, + { + "bbox": [ + 169, + 191, + 189, + 201 + ], + "score": 0.89, + "content": "1 0 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 189, + 189, + 231, + 205 + ], + "score": 1.0, + "content": "instead of", + "type": "text" + }, + { + "bbox": [ + 231, + 191, + 246, + 201 + ], + "score": 0.88, + "content": "8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 246, + 189, + 506, + 205 + ], + "score": 1.0, + "content": "on all classes. Yet, and most interestingly, if one were able to get", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 201, + 505, + 214 + ], + "spans": [ + { + "bbox": [ + 105, + 201, + 505, + 214 + ], + "score": 1.0, + "content": "the best per-class performance —as per varying the lower-bound as in Fig. 2 and picking for each", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 211, + 505, + 226 + ], + "spans": [ + { + "bbox": [ + 105, + 211, + 505, + 226 + ], + "score": 1.0, + "content": "class the best per-class performance of any of the models— one could reach 79.37. Beyond pure", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 224, + 505, + 236 + ], + "spans": [ + { + "bbox": [ + 105, + 224, + 505, + 236 + ], + "score": 1.0, + "content": "average test performance, controlling the worst-case per-class performance is of crucial importance", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 234, + 506, + 247 + ], + "spans": [ + { + "bbox": [ + 105, + 234, + 506, + 247 + ], + "score": 1.0, + "content": "for fairness [Du et al., 2020, Veitch et al., 2021]. We thus explore one of the solution that we", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 245, + 506, + 258 + ], + "spans": [ + { + "bbox": [ + 105, + 245, + 506, + 258 + ], + "score": 1.0, + "content": "reviewed in Section 3.3 that consists in only applying the random crop DA to the classes whose test", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 255, + 505, + 270 + ], + "spans": [ + { + "bbox": [ + 105, + 255, + 505, + 270 + ], + "score": 1.0, + "content": "performances increased with the DA’s level. We obtain in Fig. 7 that such class-specific strategy", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 268, + 506, + 280 + ], + "spans": [ + { + "bbox": [ + 105, + 268, + 506, + 280 + ], + "score": 1.0, + "content": "is not sufficient to guarantee that the classes negatively impacted by the random crop DA see their", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 278, + 506, + 291 + ], + "spans": [ + { + "bbox": [ + 105, + 278, + 506, + 291 + ], + "score": 1.0, + "content": "performance to be constant across the DA level applied to all the other classes. This finding is also", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 289, + 505, + 301 + ], + "spans": [ + { + "bbox": [ + 105, + 289, + 505, + 301 + ], + "score": 1.0, + "content": "supported by our weight decay experiment in Figs. 3 and 16 in which the regularization of the DN", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 299, + 505, + 313 + ], + "spans": [ + { + "bbox": [ + 105, + 299, + 505, + 313 + ], + "score": 1.0, + "content": "impacted classes differently. In fact, first notice that applying weight decay only when seeing some", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 311, + 506, + 324 + ], + "spans": [ + { + "bbox": [ + 105, + 311, + 506, + 324 + ], + "score": 1.0, + "content": "specific classes would simply amount (on average) to reducing the weight decay hyper-parameter", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 321, + 506, + 335 + ], + "spans": [ + { + "bbox": [ + 105, + 321, + 506, + 335 + ], + "score": 1.0, + "content": "proportionally to how many classes are considered to be without regularization. In a similar fashion,", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 333, + 506, + 344 + ], + "spans": [ + { + "bbox": [ + 105, + 333, + 506, + 344 + ], + "score": 1.0, + "content": "DA produces an implicit regularizer [Balestriero et al., 2022] and applying a class-specific DA level", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 343, + 505, + 356 + ], + "spans": [ + { + "bbox": [ + 105, + 343, + 505, + 356 + ], + "score": 1.0, + "content": "reduces the impact of the implicit regularizer. But since the amount of classes for which we do not", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 354, + 506, + 366 + ], + "spans": [ + { + "bbox": [ + 105, + 355, + 450, + 366 + ], + "score": 1.0, + "content": "apply random crop is quite small compared to the total number of classes (between", + "type": "text" + }, + { + "bbox": [ + 450, + 354, + 465, + 365 + ], + "score": 0.85, + "content": "1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 466, + 355, + 484, + 366 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 484, + 354, + 499, + 365 + ], + "score": 0.84, + "content": "5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 500, + 355, + 506, + 366 + ], + "score": 1.0, + "content": "),", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 365, + 505, + 377 + ], + "spans": [ + { + "bbox": [ + 105, + 365, + 505, + 377 + ], + "score": 1.0, + "content": "it means that the DA’s implicit regularizer remains nearly the same and thus the model’s bias is", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 376, + 507, + 389 + ], + "spans": [ + { + "bbox": [ + 105, + 376, + 507, + 389 + ], + "score": 1.0, + "content": "nearly the same regardless if that DA is applied or not onto those classes. This is what we observe,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 387, + 506, + 399 + ], + "spans": [ + { + "bbox": [ + 105, + 387, + 506, + 399 + ], + "score": 1.0, + "content": "applying the random crop DA to the classes that benefit from it is enough to bias the model and", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 398, + 506, + 411 + ], + "spans": [ + { + "bbox": [ + 105, + 398, + 506, + 411 + ], + "score": 1.0, + "content": "degrade the performances on some classes at the same pace than when applying the DA to all classes", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 410, + 271, + 421 + ], + "spans": [ + { + "bbox": [ + 105, + 410, + 271, + 421 + ], + "score": 1.0, + "content": "unconditionally (compare Figs. 2 and 7).", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 17.5 + }, + { + "type": "text", + "bbox": [ + 107, + 425, + 505, + 491 + ], + "lines": [ + { + "bbox": [ + 105, + 423, + 506, + 439 + ], + "spans": [ + { + "bbox": [ + 105, + 423, + 506, + 439 + ], + "score": 1.0, + "content": "As a result, we observe that no readily and easily implemented solution provides us with a strategy to", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 436, + 506, + 449 + ], + "spans": [ + { + "bbox": [ + 105, + 436, + 506, + 449 + ], + "score": 1.0, + "content": "prevent deep learning to fall into the scenario depicted on the left of Fig. 1. Those findings however", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 447, + 505, + 460 + ], + "spans": [ + { + "bbox": [ + 105, + 447, + 505, + 460 + ], + "score": 1.0, + "content": "motivate the search of novel model complexity controls that is fair among classes. From a more", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 459, + 505, + 470 + ], + "spans": [ + { + "bbox": [ + 106, + 459, + 505, + 470 + ], + "score": 1.0, + "content": "theoretical viewpoint, it might also be possible to better understand if even such a fair per-class model", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 469, + 505, + 481 + ], + "spans": [ + { + "bbox": [ + 105, + 469, + 505, + 481 + ], + "score": 1.0, + "content": "complexity could exist, which is not clear as natural image classes tend to have inherently different", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 480, + 146, + 492 + ], + "spans": [ + { + "bbox": [ + 105, + 480, + 146, + 492 + ], + "score": 1.0, + "content": "statistics.", + "type": "text" + } + ], + "index": 35 + } + ], + "index": 32.5 + }, + { + "type": "title", + "bbox": [ + 107, + 519, + 272, + 533 + ], + "lines": [ + { + "bbox": [ + 105, + 519, + 273, + 535 + ], + "spans": [ + { + "bbox": [ + 105, + 519, + 273, + 535 + ], + "score": 1.0, + "content": "4 Conclusions and Limitations", + "type": "text" + } + ], + "index": 36 + } + ], + "index": 36 + }, + { + "type": "text", + "bbox": [ + 107, + 552, + 505, + 684 + ], + "lines": [ + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "spans": [ + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "score": 1.0, + "content": "We proposed in this study to understand the impact of regularization, in particular data-augmentation", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 564, + 506, + 576 + ], + "spans": [ + { + "bbox": [ + 106, + 564, + 506, + 576 + ], + "score": 1.0, + "content": "and weight decay, into the final performances of a deep network. We obtained that the use of", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 574, + 505, + 587 + ], + "spans": [ + { + "bbox": [ + 105, + 574, + 505, + 587 + ], + "score": 1.0, + "content": "regularization increases the average test performances at the cost of significant performance drops on", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 585, + 505, + 598 + ], + "spans": [ + { + "bbox": [ + 105, + 585, + 505, + 598 + ], + "score": 1.0, + "content": "some specific classes. By focusing on maximizing aggregate performance statistics we have produced", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 595, + 506, + 609 + ], + "spans": [ + { + "bbox": [ + 105, + 595, + 506, + 609 + ], + "score": 1.0, + "content": "learning mechanisms that can be potentially harmful, especially in transfer learning tasks. In fact,", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 608, + 506, + 620 + ], + "spans": [ + { + "bbox": [ + 106, + 608, + 506, + 620 + ], + "score": 1.0, + "content": "we have also observed that varying the amount of regularization employed during pre-training of a", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 618, + 506, + 631 + ], + "spans": [ + { + "bbox": [ + 105, + 618, + 506, + 631 + ], + "score": 1.0, + "content": "specific dataset impacts the per-class performances of that pre-trained model on different downstream", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 106, + 628, + 506, + 642 + ], + "spans": [ + { + "bbox": [ + 106, + 628, + 506, + 642 + ], + "score": 1.0, + "content": "tasks e.g. going from Imagenet to INaturalist. Lastly, commonly prescribed solutions e.g. class-", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 640, + 505, + 653 + ], + "spans": [ + { + "bbox": [ + 105, + 640, + 505, + 653 + ], + "score": 1.0, + "content": "dependent data-augmentation do not seem to help indicating that the sole use of an augmentation on", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 104, + 650, + 506, + 664 + ], + "spans": [ + { + "bbox": [ + 104, + 650, + 506, + 664 + ], + "score": 1.0, + "content": "some classes is enough to bias the model on all classes. Hence, there remains a vast research area to", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 106, + 662, + 505, + 675 + ], + "spans": [ + { + "bbox": [ + 106, + 662, + 505, + 675 + ], + "score": 1.0, + "content": "explore in order to turn deep learning model selection from the current regime to a more ideal one", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 106, + 673, + 199, + 685 + ], + "spans": [ + { + "bbox": [ + 106, + 673, + 199, + 685 + ], + "score": 1.0, + "content": "(left to right of Fig. 1).", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 42.5 + }, + { + "type": "text", + "bbox": [ + 108, + 689, + 504, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 688, + 505, + 701 + ], + "spans": [ + { + "bbox": [ + 106, + 688, + 505, + 701 + ], + "score": 1.0, + "content": "The main limitation of this work is its focus on computer vision datasets and models. It is possible", + "type": "text" + } + ], + "index": 49 + }, + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "that our observation will be further conveyed in other regimes or not, and we leave such analysis for", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 106, + 710, + 158, + 722 + ], + "spans": [ + { + "bbox": [ + 106, + 710, + 158, + 722 + ], + "score": 1.0, + "content": "future work.", + "type": "text" + } + ], + "index": 51 + } + ], + "index": 50 + } + ], + "page_idx": 8, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 742, + 308, + 750 + ], + "lines": [ + { + "bbox": [ + 302, + 741, + 309, + 752 + ], + "spans": [ + { + "bbox": [ + 302, + 741, + 309, + 752 + ], + "score": 1.0, + "content": "9", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "title", + "bbox": [ + 105, + 72, + 483, + 84 + ], + "lines": [ + { + "bbox": [ + 104, + 69, + 484, + 88 + ], + "spans": [ + { + "bbox": [ + 104, + 69, + 484, + 88 + ], + "score": 1.0, + "content": "3.4 Class-Dependent Data-Augmentation Seems Insufficient for Performance Recovery", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "text", + "bbox": [ + 107, + 97, + 505, + 153 + ], + "lines": [ + { + "bbox": [ + 106, + 97, + 506, + 111 + ], + "spans": [ + { + "bbox": [ + 106, + 97, + 506, + 111 + ], + "score": 1.0, + "content": "We observed in Section 2 that DA could lead to disastrous per-class performances on a minority of", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 108, + 505, + 122 + ], + "spans": [ + { + "bbox": [ + 105, + 108, + 505, + 122 + ], + "score": 1.0, + "content": "classes. We now propose to implement one solution from Section 3.3 that consists in simply not", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 119, + 506, + 133 + ], + "spans": [ + { + "bbox": [ + 105, + 119, + 506, + 133 + ], + "score": 1.0, + "content": "applying the harmful DA to the classes suffering from it. As will become clear, applying the DA on", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 129, + 505, + 145 + ], + "spans": [ + { + "bbox": [ + 105, + 129, + 505, + 145 + ], + "score": 1.0, + "content": "all other classes will be enough to skew the training of the model preventing any performing recovery", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 141, + 176, + 154 + ], + "spans": [ + { + "bbox": [ + 105, + 141, + 176, + 154 + ], + "score": 1.0, + "content": "on those classes.", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 3, + "bbox_fs": [ + 105, + 97, + 506, + 154 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 158, + 505, + 420 + ], + "lines": [ + { + "bbox": [ + 105, + 158, + 505, + 171 + ], + "spans": [ + { + "bbox": [ + 105, + 158, + 505, + 171 + ], + "score": 1.0, + "content": "To motivate the need for a better control of the per-class performance of a model, we first present an", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 167, + 505, + 183 + ], + "spans": [ + { + "bbox": [ + 105, + 167, + 365, + 183 + ], + "score": 1.0, + "content": "illustrative argument. A standard resnet50 on Imagenet reaches", + "type": "text" + }, + { + "bbox": [ + 366, + 169, + 398, + 180 + ], + "score": 0.89, + "content": "7 7 . 1 4 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 398, + 167, + 505, + 183 + ], + "score": 1.0, + "content": "top-1 with a random crop", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 178, + 505, + 194 + ], + "spans": [ + { + "bbox": [ + 105, + 178, + 170, + 194 + ], + "score": 1.0, + "content": "lower bound of", + "type": "text" + }, + { + "bbox": [ + 170, + 180, + 185, + 190 + ], + "score": 0.87, + "content": "8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 185, + 178, + 505, + 194 + ], + "score": 1.0, + "content": ". Using precise cross-validation, one could reach 77.29 by using a random crop", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 189, + 506, + 205 + ], + "spans": [ + { + "bbox": [ + 105, + 189, + 169, + 205 + ], + "score": 1.0, + "content": "lower bound of", + "type": "text" + }, + { + "bbox": [ + 169, + 191, + 189, + 201 + ], + "score": 0.89, + "content": "1 0 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 189, + 189, + 231, + 205 + ], + "score": 1.0, + "content": "instead of", + "type": "text" + }, + { + "bbox": [ + 231, + 191, + 246, + 201 + ], + "score": 0.88, + "content": "8 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 246, + 189, + 506, + 205 + ], + "score": 1.0, + "content": "on all classes. Yet, and most interestingly, if one were able to get", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 201, + 505, + 214 + ], + "spans": [ + { + "bbox": [ + 105, + 201, + 505, + 214 + ], + "score": 1.0, + "content": "the best per-class performance —as per varying the lower-bound as in Fig. 2 and picking for each", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 211, + 505, + 226 + ], + "spans": [ + { + "bbox": [ + 105, + 211, + 505, + 226 + ], + "score": 1.0, + "content": "class the best per-class performance of any of the models— one could reach 79.37. Beyond pure", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 224, + 505, + 236 + ], + "spans": [ + { + "bbox": [ + 105, + 224, + 505, + 236 + ], + "score": 1.0, + "content": "average test performance, controlling the worst-case per-class performance is of crucial importance", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 234, + 506, + 247 + ], + "spans": [ + { + "bbox": [ + 105, + 234, + 506, + 247 + ], + "score": 1.0, + "content": "for fairness [Du et al., 2020, Veitch et al., 2021]. We thus explore one of the solution that we", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 245, + 506, + 258 + ], + "spans": [ + { + "bbox": [ + 105, + 245, + 506, + 258 + ], + "score": 1.0, + "content": "reviewed in Section 3.3 that consists in only applying the random crop DA to the classes whose test", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 255, + 505, + 270 + ], + "spans": [ + { + "bbox": [ + 105, + 255, + 505, + 270 + ], + "score": 1.0, + "content": "performances increased with the DA’s level. We obtain in Fig. 7 that such class-specific strategy", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 268, + 506, + 280 + ], + "spans": [ + { + "bbox": [ + 105, + 268, + 506, + 280 + ], + "score": 1.0, + "content": "is not sufficient to guarantee that the classes negatively impacted by the random crop DA see their", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 278, + 506, + 291 + ], + "spans": [ + { + "bbox": [ + 105, + 278, + 506, + 291 + ], + "score": 1.0, + "content": "performance to be constant across the DA level applied to all the other classes. This finding is also", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 289, + 505, + 301 + ], + "spans": [ + { + "bbox": [ + 105, + 289, + 505, + 301 + ], + "score": 1.0, + "content": "supported by our weight decay experiment in Figs. 3 and 16 in which the regularization of the DN", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 299, + 505, + 313 + ], + "spans": [ + { + "bbox": [ + 105, + 299, + 505, + 313 + ], + "score": 1.0, + "content": "impacted classes differently. In fact, first notice that applying weight decay only when seeing some", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 311, + 506, + 324 + ], + "spans": [ + { + "bbox": [ + 105, + 311, + 506, + 324 + ], + "score": 1.0, + "content": "specific classes would simply amount (on average) to reducing the weight decay hyper-parameter", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 321, + 506, + 335 + ], + "spans": [ + { + "bbox": [ + 105, + 321, + 506, + 335 + ], + "score": 1.0, + "content": "proportionally to how many classes are considered to be without regularization. In a similar fashion,", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 333, + 506, + 344 + ], + "spans": [ + { + "bbox": [ + 105, + 333, + 506, + 344 + ], + "score": 1.0, + "content": "DA produces an implicit regularizer [Balestriero et al., 2022] and applying a class-specific DA level", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 343, + 505, + 356 + ], + "spans": [ + { + "bbox": [ + 105, + 343, + 505, + 356 + ], + "score": 1.0, + "content": "reduces the impact of the implicit regularizer. But since the amount of classes for which we do not", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 354, + 506, + 366 + ], + "spans": [ + { + "bbox": [ + 105, + 355, + 450, + 366 + ], + "score": 1.0, + "content": "apply random crop is quite small compared to the total number of classes (between", + "type": "text" + }, + { + "bbox": [ + 450, + 354, + 465, + 365 + ], + "score": 0.85, + "content": "1 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 466, + 355, + 484, + 366 + ], + "score": 1.0, + "content": "and", + "type": "text" + }, + { + "bbox": [ + 484, + 354, + 499, + 365 + ], + "score": 0.84, + "content": "5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 500, + 355, + 506, + 366 + ], + "score": 1.0, + "content": "),", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 365, + 505, + 377 + ], + "spans": [ + { + "bbox": [ + 105, + 365, + 505, + 377 + ], + "score": 1.0, + "content": "it means that the DA’s implicit regularizer remains nearly the same and thus the model’s bias is", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 376, + 507, + 389 + ], + "spans": [ + { + "bbox": [ + 105, + 376, + 507, + 389 + ], + "score": 1.0, + "content": "nearly the same regardless if that DA is applied or not onto those classes. This is what we observe,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 387, + 506, + 399 + ], + "spans": [ + { + "bbox": [ + 105, + 387, + 506, + 399 + ], + "score": 1.0, + "content": "applying the random crop DA to the classes that benefit from it is enough to bias the model and", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 398, + 506, + 411 + ], + "spans": [ + { + "bbox": [ + 105, + 398, + 506, + 411 + ], + "score": 1.0, + "content": "degrade the performances on some classes at the same pace than when applying the DA to all classes", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 410, + 271, + 421 + ], + "spans": [ + { + "bbox": [ + 105, + 410, + 271, + 421 + ], + "score": 1.0, + "content": "unconditionally (compare Figs. 2 and 7).", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 17.5, + "bbox_fs": [ + 105, + 158, + 507, + 421 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 425, + 505, + 491 + ], + "lines": [ + { + "bbox": [ + 105, + 423, + 506, + 439 + ], + "spans": [ + { + "bbox": [ + 105, + 423, + 506, + 439 + ], + "score": 1.0, + "content": "As a result, we observe that no readily and easily implemented solution provides us with a strategy to", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 436, + 506, + 449 + ], + "spans": [ + { + "bbox": [ + 105, + 436, + 506, + 449 + ], + "score": 1.0, + "content": "prevent deep learning to fall into the scenario depicted on the left of Fig. 1. Those findings however", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 447, + 505, + 460 + ], + "spans": [ + { + "bbox": [ + 105, + 447, + 505, + 460 + ], + "score": 1.0, + "content": "motivate the search of novel model complexity controls that is fair among classes. From a more", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 459, + 505, + 470 + ], + "spans": [ + { + "bbox": [ + 106, + 459, + 505, + 470 + ], + "score": 1.0, + "content": "theoretical viewpoint, it might also be possible to better understand if even such a fair per-class model", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 469, + 505, + 481 + ], + "spans": [ + { + "bbox": [ + 105, + 469, + 505, + 481 + ], + "score": 1.0, + "content": "complexity could exist, which is not clear as natural image classes tend to have inherently different", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 480, + 146, + 492 + ], + "spans": [ + { + "bbox": [ + 105, + 480, + 146, + 492 + ], + "score": 1.0, + "content": "statistics.", + "type": "text" + } + ], + "index": 35 + } + ], + "index": 32.5, + "bbox_fs": [ + 105, + 423, + 506, + 492 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 519, + 272, + 533 + ], + "lines": [ + { + "bbox": [ + 105, + 519, + 273, + 535 + ], + "spans": [ + { + "bbox": [ + 105, + 519, + 273, + 535 + ], + "score": 1.0, + "content": "4 Conclusions and Limitations", + "type": "text" + } + ], + "index": 36 + } + ], + "index": 36 + }, + { + "type": "text", + "bbox": [ + 107, + 552, + 505, + 684 + ], + "lines": [ + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "spans": [ + { + "bbox": [ + 106, + 553, + 505, + 565 + ], + "score": 1.0, + "content": "We proposed in this study to understand the impact of regularization, in particular data-augmentation", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 564, + 506, + 576 + ], + "spans": [ + { + "bbox": [ + 106, + 564, + 506, + 576 + ], + "score": 1.0, + "content": "and weight decay, into the final performances of a deep network. We obtained that the use of", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 574, + 505, + 587 + ], + "spans": [ + { + "bbox": [ + 105, + 574, + 505, + 587 + ], + "score": 1.0, + "content": "regularization increases the average test performances at the cost of significant performance drops on", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 585, + 505, + 598 + ], + "spans": [ + { + "bbox": [ + 105, + 585, + 505, + 598 + ], + "score": 1.0, + "content": "some specific classes. By focusing on maximizing aggregate performance statistics we have produced", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 595, + 506, + 609 + ], + "spans": [ + { + "bbox": [ + 105, + 595, + 506, + 609 + ], + "score": 1.0, + "content": "learning mechanisms that can be potentially harmful, especially in transfer learning tasks. In fact,", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 608, + 506, + 620 + ], + "spans": [ + { + "bbox": [ + 106, + 608, + 506, + 620 + ], + "score": 1.0, + "content": "we have also observed that varying the amount of regularization employed during pre-training of a", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 618, + 506, + 631 + ], + "spans": [ + { + "bbox": [ + 105, + 618, + 506, + 631 + ], + "score": 1.0, + "content": "specific dataset impacts the per-class performances of that pre-trained model on different downstream", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 106, + 628, + 506, + 642 + ], + "spans": [ + { + "bbox": [ + 106, + 628, + 506, + 642 + ], + "score": 1.0, + "content": "tasks e.g. going from Imagenet to INaturalist. Lastly, commonly prescribed solutions e.g. class-", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 640, + 505, + 653 + ], + "spans": [ + { + "bbox": [ + 105, + 640, + 505, + 653 + ], + "score": 1.0, + "content": "dependent data-augmentation do not seem to help indicating that the sole use of an augmentation on", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 104, + 650, + 506, + 664 + ], + "spans": [ + { + "bbox": [ + 104, + 650, + 506, + 664 + ], + "score": 1.0, + "content": "some classes is enough to bias the model on all classes. Hence, there remains a vast research area to", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 106, + 662, + 505, + 675 + ], + "spans": [ + { + "bbox": [ + 106, + 662, + 505, + 675 + ], + "score": 1.0, + "content": "explore in order to turn deep learning model selection from the current regime to a more ideal one", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 106, + 673, + 199, + 685 + ], + "spans": [ + { + "bbox": [ + 106, + 673, + 199, + 685 + ], + "score": 1.0, + "content": "(left to right of Fig. 1).", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 42.5, + "bbox_fs": [ + 104, + 553, + 506, + 685 + ] + }, + { + "type": "text", + "bbox": [ + 108, + 689, + 504, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 688, + 505, + 701 + ], + "spans": [ + { + "bbox": [ + 106, + 688, + 505, + 701 + ], + "score": 1.0, + "content": "The main limitation of this work is its focus on computer vision datasets and models. 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For all authors...", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 9 + }, + { + "type": "text", + "bbox": [ + 146, + 224, + 505, + 328 + ], + "lines": [ + { + "bbox": [ + 145, + 223, + 505, + 236 + ], + "spans": [ + { + "bbox": [ + 145, + 223, + 505, + 236 + ], + "score": 1.0, + "content": "(a) Do the main claims made in the abstract and introduction accurately reflect the paper’s", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 162, + 235, + 288, + 247 + ], + "spans": [ + { + "bbox": [ + 162, + 235, + 288, + 247 + ], + "score": 1.0, + "content": "contributions and scope? [Yes]", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 144, + 247, + 506, + 261 + ], + "spans": [ + { + "bbox": [ + 144, + 247, + 506, + 261 + ], + "score": 1.0, + "content": "(b) Did you describe the limitations of your work? [N/A] We do not propose a novel", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 162, + 258, + 312, + 270 + ], + "spans": [ + { + "bbox": [ + 162, + 258, + 312, + 270 + ], + "score": 1.0, + "content": "method that requires such discussion", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 146, + 270, + 506, + 283 + ], + "spans": [ + { + "bbox": [ + 146, + 270, + 506, + 283 + ], + "score": 1.0, + "content": "(c) Did you discuss any potential negative societal impacts of your work? [Yes] We", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 162, + 282, + 505, + 294 + ], + "spans": [ + { + "bbox": [ + 162, + 282, + 505, + 294 + ], + "score": 1.0, + "content": "carefully described why the observations we have made can be dangerous for real", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 162, + 294, + 237, + 304 + ], + "spans": [ + { + "bbox": [ + 162, + 294, + 237, + 304 + ], + "score": 1.0, + "content": "world applications", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 145, + 305, + 505, + 318 + ], + "spans": [ + { + "bbox": [ + 145, + 305, + 505, + 318 + ], + "score": 1.0, + "content": "(d) Have you read the ethics review guidelines and ensured that your paper conforms to", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 161, + 316, + 214, + 329 + ], + "spans": [ + { + "bbox": [ + 161, + 316, + 214, + 329 + ], + "score": 1.0, + "content": "them? [Yes]", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 14 + }, + { + "type": "text", + "bbox": [ + 131, + 331, + 302, + 343 + ], + "lines": [ + { + "bbox": [ + 129, + 330, + 304, + 344 + ], + "spans": [ + { + "bbox": [ + 129, + 330, + 304, + 344 + ], + "score": 1.0, + "content": "2. If you are including theoretical results...", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 19 + }, + { + "type": "text", + "bbox": [ + 146, + 345, + 505, + 402 + ], + "lines": [ + { + "bbox": [ + 146, + 345, + 505, + 358 + ], + "spans": [ + { + "bbox": [ + 146, + 345, + 505, + 358 + ], + "score": 1.0, + "content": "(a) Did you state the full set of assumptions of all theoretical results? [Yes] Our only", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 161, + 355, + 505, + 369 + ], + "spans": [ + { + "bbox": [ + 161, + 355, + 505, + 369 + ], + "score": 1.0, + "content": "“theoretical result” consists in a formal statistical test for which we precisely describe", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 162, + 368, + 265, + 379 + ], + "spans": [ + { + "bbox": [ + 162, + 368, + 265, + 379 + ], + "score": 1.0, + "content": "our settings and statistics", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 146, + 379, + 506, + 393 + ], + "spans": [ + { + "bbox": [ + 146, + 379, + 506, + 393 + ], + "score": 1.0, + "content": "(b) Did you include complete proofs of all theoretical results? [N/A] No theoretical result", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 161, + 392, + 285, + 403 + ], + "spans": [ + { + "bbox": [ + 161, + 392, + 285, + 403 + ], + "score": 1.0, + "content": "requiring proofs was provided", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 22 + }, + { + "type": "text", + "bbox": [ + 131, + 406, + 241, + 417 + ], + "lines": [ + { + "bbox": [ + 128, + 403, + 243, + 419 + ], + "spans": [ + { + "bbox": [ + 128, + 403, + 243, + 419 + ], + "score": 1.0, + "content": "3. If you ran experiments...", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 25 + }, + { + "type": "text", + "bbox": [ + 145, + 420, + 505, + 589 + ], + "lines": [ + { + "bbox": [ + 147, + 419, + 506, + 433 + ], + "spans": [ + { + "bbox": [ + 147, + 419, + 506, + 433 + ], + "score": 1.0, + "content": "(a) Did you include the code, data, and instructions needed to reproduce the main exper-", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 161, + 431, + 506, + 443 + ], + "spans": [ + { + "bbox": [ + 161, + 431, + 506, + 443 + ], + "score": 1.0, + "content": "imental results (either in the supplemental material or as a URL)? [Yes] We include", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 162, + 442, + 505, + 454 + ], + "spans": [ + { + "bbox": [ + 162, + 442, + 505, + 454 + ], + "score": 1.0, + "content": "summary statistics in the supplementary material which is enough to validate our", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 162, + 453, + 505, + 465 + ], + "spans": [ + { + "bbox": [ + 162, + 453, + 505, + 465 + ], + "score": 1.0, + "content": "claims. The full codebase and all the saved models will be released upon completion", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 162, + 464, + 469, + 476 + ], + "spans": [ + { + "bbox": [ + 162, + 464, + 469, + 476 + ], + "score": 1.0, + "content": "of the review process (this includes almost a thousand pre-trained resnet50s)", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 146, + 476, + 505, + 490 + ], + "spans": [ + { + "bbox": [ + 146, + 476, + 505, + 490 + ], + "score": 1.0, + "content": "(b) Did you specify all the training details (e.g., data splits, hyperparameters, how they", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 161, + 486, + 506, + 501 + ], + "spans": [ + { + "bbox": [ + 161, + 486, + 506, + 501 + ], + "score": 1.0, + "content": "were chosen)? 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