repo stringlengths 8 116 | tasks stringlengths 8 117 | titles stringlengths 17 302 | dependencies stringlengths 5 372k | readme stringlengths 5 4.26k | __index_level_0__ int64 0 4.36k |
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CapsuleEndoscope/VirtualCapsuleEndoscopy | ['depth estimation', 'visual localization'] | ['VR-Caps: A Virtual Environment for Capsule Endoscopy'] | Tasks/Disease Classification/classification_pytorch.py Tasks/Pose and Depth Estimation/poseError.py train_model save_model prepare_prediction_file test_model prepare_data prepare_submission_file prepare_model check_model_graph plot_confusion_matrix run_train translation_error rotation_error rotate_origin_only compute_A... | VR-Caps: A Virtual Environment for Capsule Endoscopy ===== <p align="center"> <img src='img/logo_turan.png'> </p> <p align="center"> <img src='img/Fig1.png' width=512/> </p> ## Overview We introduce a virtual active capsule endoscopy environment developed in Unity that provides a simulation platform to generate synth... | 200 |
Carco-git/CW_Attack_on_MNIST | ['adversarial attack'] | ['Towards Evaluating the Robustness of Neural Networks'] | MNIST_Model.py MNIST_Model | # CW_Attack_on_MNIST Implemention of cw attack on pytorch with corresponding MNIST model ## MNIST model Based on [Towards Evaluating the Robustness of Neural Networks](https://arxiv.org/abs/1608.04644) TABLE1 Consist of four convolution layer, two pooling layers, tow FC layers and ReLU. Notice: softmax shouldn't be put... | 201 |
CardiacModelling/fickleheart-method-tutorials | ['gaussian processes'] | ['Considering discrepancy when calibrating a mechanistic electrophysiology model'] | ion-channel-models/compare-posteriors-gpcovs.py ion-channel-models/plot-residual-noise.py ion-channel-models/fit-gp-v.py ion-channel-models/method/sparse_gp_custom_likelihood.py ion-channel-models/compare-pp.py ion-channel-models/protocol-time-series/staircase_to_zoom.py ion-channel-models/method/sparse_gp_custom_likel... | # Model calibration with discrepancy This repo contains the code for reproducing the results in the examples in the paper "*Considering discrepancy when calibrating a mechanistic electrophysiology model*" by Lei, Ghosh, Whittaker, Aboelkassem, Beattie, Cantwell, Delhaas, Houston, Novaes, Panfilov, Pathmanathan, Riabiz... | 202 |
Cartus/AGGCN | ['relation extraction'] | ['Attention Guided Graph Convolutional Networks for Relation Extraction'] | semeval/utils/helper.py utils/vocab.py PubMed/Binary/train.py semeval/model/trainer.py semeval/prepare_vocab.py PubMed/Tenary/utils/nary_scorer.py PubMed/Tenary/eval.py prepare_vocab.py semeval/data/loader.py PubMed/Tenary/utils/vocab.py PubMed/Binary/model/trainer.py train.py PubMed/Tenary/prepare_vocab.py semeval/eva... | Attention Guided Graph Convolutional Networks for Relation Extraction ========== This paper/code introduces the Attention Guided Graph Convolutional graph convolutional networks (AGGCNs) over dependency trees for the large scale sentence-level relation extraction task (TACRED). You can find the paper [here](https://arx... | 203 |
Cartus/AGGCN_TACRED | ['relation extraction'] | ['Attention Guided Graph Convolutional Networks for Relation Extraction'] | semeval/utils/helper.py utils/vocab.py PubMed/Binary/train.py semeval/model/trainer.py semeval/prepare_vocab.py PubMed/Tenary/utils/nary_scorer.py PubMed/Tenary/eval.py prepare_vocab.py semeval/data/loader.py PubMed/Tenary/utils/vocab.py PubMed/Binary/model/trainer.py train.py PubMed/Tenary/prepare_vocab.py semeval/eva... | Attention Guided Graph Convolutional Networks for Relation Extraction ========== This paper/code introduces the Attention Guided Graph Convolutional graph convolutional networks (AGGCNs) over dependency trees for the large scale sentence-level relation extraction task (TACRED). You can find the paper [here](https://arx... | 204 |
Cassieyy/CLCINet_-MICCAI2019- | ['medical image segmentation', 'lesion segmentation', 'semantic segmentation'] | ['CLCI-Net: Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke'] | ConvLSTM.py CLCINet.py DoubleConv conv_lstm conv_1_init dilate_conv CLCInet concat_pool ConvLSTMCell ConvLSTM | # CLCINet_-MICCAI2019- CLCINet(Cross-Level fusion and Context Inference Networks for Lesion Segmentation of Chronic Stroke) implemented by pytorch. paper link:https://arxiv.org/abs/1907.07008 implemented by tensoflow(official):https://github.com/YH0517/CLCI_Net/blob/master/CLCI_Net.py | 205 |
CedricTravelletti/MESLAS | ['experimental design'] | ['Learning excursion sets of vector-valued Gaussian random fields for autonomous ocean sampling'] | reporting/paper_figures/ebv_comparison/plot_eibv_heteroropic_onefig.py meslas/sensor.py meslas/covariance/cross_covariances.py meslas/tests/test_mvnorm.py examples/sample_and_plot.py reporting/paper_figures/intro_excursion_set/plot_excursion.py examples/full_example.py meslas/covariance/tests/test_heterotopic.py meslas... | # MESLAS: Multivariate Excursion Set Learning by Adaptive Sampling The MESLAS package provides functionalities for simulation of multivariate gaussian random fields, as well as adaptive sampling startegies to learn excursion sets thereof. It originated as part of a collaboration between NTNU Trondheim and University of... | 206 |
CellEight/Pytorch-Adaptive-Instance-Normalization | ['style transfer'] | ['Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization'] | train.py style.py AdaIN.py utils.py model.py AdaIN AdaINStyle debugHook styleHook loadImage debugHook AdaINLoss styleHook ImageDataset mu sigma preprocess Compose convert | # Pytorch-Adaptive-Instance-Normalization A Pytorch implementation of the 2017 Huang et. al. paper "Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization" [https://arxiv.org/abs/1703.06868](https://arxiv.org/abs/1703.06868) Written from scratch with essentially no reference to Xun Huangs implementa... | 207 |
Cerenaut/aha | ['one shot learning'] | ['Unsupervised One-shot Learning of Both Specific Instances and Generalised Classes with a Hippocampal Architecture'] | train_ltm.py aha/components/hopfieldlike_component.py aha/components/dg_sae.py aha/utils/generic_utils.py aha/utils/interest_filter.py plot_results.py aha/utils/recursive_component_harness.py aha/workflows/episodic_pattern_completion_workflow.py aha/workflows/dp_workflow.py aha/components/episodic_component.py plot_res... | # Artificial Hippocampus Algorithm The codebase for the Artificial Hippocampus Algorithm (AHA) project. ## Dependencies - [PAGI Framework](https://github.com/ProjectAGI/pagi) >= 0.1 ## Getting Started Ensure that you have `pagi` installed and that its accessible via the command-line by running `pagi --help`. Clone this... | 208 |
ChWick/ocropy | ['optical character recognition'] | ['Improving OCR Accuracy on Early Printed Books using Deep Convolutional Networks'] | OLD/ocrolib/ngraphs.py OLD/mlp.py OLD/test-feature-extractor.py ocrolib/psegutils.py ocrolib/__init__.py ocrolib/ligatures.py ocrolib/lstm.py ocrolib/lang.py OLD/lineproc.py ocrolib/utils.py OLD/wmodel.py ocrolib/chars.py ocrolib/extras/fgen.py ocrolib/extras/cairoextras.py ocrolib/sl.py OLD/distance.py ocrolib/lineest... | ocropy ====== [](https://travis-ci.org/tmbdev/ocropy) [](https://circleci.com/gh/UB-Mannheim/ocropy.png) [ to get the full codes of GaitPart！！！*** GaitPart is a CVPR2020 paper, and some of the source code is already opened while the rest will be released as soon as possible! ## Updates ... | 210 |
ChaoYang93/GraduatePaper | ['time series analysis', 'time series'] | ['Gated Res2Net for Multivariate Time Series Analysis'] | GRes2Net.py FCRSN.py CRCN.py conv5 conv1x1 dilationblock2 ception2 adoptblock dilationblock1 dilation conv3x3 ception1 inceptionblock ception3 dilationblock3 conv1x1 conv3x1 Res2NetBottleneck Res2RNN conv5x1 SEModule conv3 GatedFCN GatedRes2NetBottleneck conv1 SEModule | # GraduatePaperA Codes for the model CRCN,FCRSN and GRes2Net. Trained model is available on: https://pan.baidu.com/s/1LaRqOQWUBZjsTaKG80tY9Q. For downloading, the extraction code is fj9a Thanks for reading. | 211 |
Charlesthebird/BOXES-DroneDelivery-ML-Agents | ['unity'] | ['Unity: A General Platform for Intelligent Agents'] | gym-unity/tests/test_gym.py gym-unity/gym_unity/__init__.py gym-unity/gym_unity/envs/__init__.py gym-unity/setup.py gym-unity/gym_unity/envs/unity_env.py UnityGymException UnityEnv test_gym_wrapper test_multi_agent sample step MockCommunicator UnityEnv step MockCommunicator UnityEnv | <img src="docs/images/unity-wide.png" align="middle" width="3000"/> <img src="docs/images/image-banner.png" align="middle" width="3000"/> # Unity ML-Agents Toolkit (Beta) **The Unity Machine Learning Agents Toolkit** (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments ... | 212 |
ChenXiao61/Img_augmentor | ['data augmentation', 'image augmentation'] | ['Augmentor: An Image Augmentation Library for Machine Learning'] | Augmentor/ImageSource.py tests/test_ground_truth_by_class.py tests/test_multi_threading.py tests/test_datapipeline.py tests/util_funcs.py tests/test_pipeline_add_operations.py tests/test_rotate.py tests/test_generators.py tests/test_user_operation_parameter_input.py docs/conf.py tests/test_load.py Augmentor/Pipeline.py... |  Augmentor is an image augmentation library in Python for machine learning. It aims to be a standalone library that is platform and framework independent, which is more convenient, allows for finer grained control over augmen... | 213 |
Cheneng/DPCNN | ['sentiment analysis', 'text classification'] | ['Deep Pyramid Convolutional Neural Networks for Text Categorization'] | data/__init__.py model/BasicModule.py model/DPCNN.py config.py data/dataset.py main.py Config TextDataset BasicModule DPCNN | # Deep Pyramid Convolutional Neural Networks for Text Categorization > This is a simple version of the paper *Deep Pyramid Convolutional Neural Networks for Text Categorization*.  You should rewrite the Dataset class in the data/dataset.py and put your data in '/data/train' or any ot... | 214 |
ChengBinJin/Adam-Analysis-TensorFlow | ['stochastic optimization'] | ['Adam: A Method for Stochastic Optimization'] | src/download.py src/dataset.py src/solver.py src/cifar10.py src/main.py src/cache.py src/mnist.py src/models.py src/utils.py src/tensorflow_utils.py cache ExpensiveClass expensive_function convert_numpy2pickle maybe_download_and_extract CIFAR10 load_cached one_hot_encoded DataSet _print_download_progress download maybe... | # Adam-Analysis-TensorFlow This repository is an evaluation of Kingma's ["ADAM: A Method for Stochastic Optimization"](https://arxiv.org/pdf/1412.6980.pdf) adam optimizer and others. <p align="center"> <img src="https://user-images.githubusercontent.com/37034031/57378858-98b32100-71e0-11e9-92c9-62c20e9a167e.png" widt... | 215 |
ChienYiChi/kaggle-panda-challenge | ['visual place recognition', 'image retrieval'] | ['NetVLAD: CNN architecture for weakly supervised place recognition'] | src/pycls/core/net.py src/pycls/datasets/loader.py src/pycls/datasets/cifar10.py src/train.py src/model.py src/pycls/datasets/imagenet.py src/pycls/core/checkpoint.py src/pycls/core/benchmark.py src/pycls/models/resnet.py src/test.py src/pycls/core/plotting.py src/pycls/core/io.py src/preprocess.py src/pycls/models/any... | ChienYiChi/kaggle-panda-challenge | 216 |
ChingtingC/Code-Switching-Sentence-Generation-by-GAN | ['data augmentation'] | ['Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation'] | train.py build_model.py tool/tag_pos.py generate.py utils.py tool/calculate_cs_rate.py main GAN train_for_n max_action translate_output2 make_trainable translate write_log evaluate_acc get_action str2bool translate_output EMBEDDING_POS generator HIDDEN_SIZE_G DROPOUT_RATE add_argument GAN NOISE_SIZE summary HIDDEN_SIZE... | # Code switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation Ching-Ting Chang, Shun-Po Chuang, Hung-Yi Lee [Interspeech 2019](https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3214.pdf) [arXiv:1811.02356](https://arxiv.org/abs/1811.02356) ## Abstract Code-swit... | 217 |
ChingtingC/Code-switching-Sentence-Generation-by-Generative-Adversarial-Networks-and-its-Application-to-Data-Au | ['data augmentation'] | ['Code-switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation'] | train.py build_model.py tool/tag_pos.py generate.py utils.py tool/calculate_cs_rate.py main GAN train_for_n max_action translate_output2 make_trainable translate write_log evaluate_acc get_action str2bool translate_output EMBEDDING_POS generator HIDDEN_SIZE_G DROPOUT_RATE add_argument GAN NOISE_SIZE summary HIDDEN_SIZE... | # Code switching Sentence Generation by Generative Adversarial Networks and its Application to Data Augmentation Ching-Ting Chang, Shun-Po Chuang, Hung-Yi Lee [Interspeech 2019](https://www.isca-speech.org/archive/Interspeech_2019/pdfs/3214.pdf) [arXiv:1811.02356](https://arxiv.org/abs/1811.02356) ## Abstract Code-swit... | 218 |
ChrisLee63/person_search | ['person search', 'person re identification', 'pedestrian detection'] | ['Joint Detection and Identification Feature Learning for Person Search'] | lib/rpn/generate_anchors.py lib/models/network.py lib/utils/boxes.py lib/oim/unlabeled_matching_layer.py tools/demo.py lib/utils/config.py lib/oim/labeled_matching_layer.py lib/datasets/sampler.py lib/rpn/proposal_layer.py lib/rpn/anchor_target_layer.py lib/models/backbone.py tools/train_net.py lib/datasets/data_proces... | # Person Search ## :sparkles: News: We release the [source code](https://github.com/serend1p1ty/SeqNet.git) of the current state-of-the-art model [SeqNet(AAAI 2021)](https://arxiv.org/abs/2103.10148), which achieves :trophy: `94.8%` mAP on CUHK-SYSU. ## Introduction A pytorch implementation for CVPR 2017 "Joint Detecti... | 219 |
ChristianMarzahl/Exact | ['whole slide images'] | ['EXACT: A collaboration toolset for algorithm-aided annotation of images with annotation version control'] | exact/exact/images/migrations/0014_auto_20180629_2250.py exact/exact/annotations/api_views.py exact/exact/images/serializers.py exact/exact/annotations/migrations/0003_auto_20170826_1207.py exact/exact/annotations/models.py exact/exact/annotations/migrations/0010_auto_20170828_1628.py exact/exact/images/api_views.py ex... | # Exact [](https://pypi.python.org/pypi/EXCAT-Sync/) [](https://lbesson.mit-license.org/) This is a collaborative online tool for labeling image data. ](https://arxiv.org/abs/2011.09763) [](https://github.com/ChristophReich1996/Cell... | 221 |
ChunpingQiu/Human-settlement-extent-detection-from-Sentinel-2-images-via-fully-convolutional-neural-networks- | ['semantic segmentation'] | ['A framework for large-scale mapping of human settlement extent from Sentinel-2 images via fully convolutional neural networks'] | modelSelection.py dataGener.py train.py img2mapC.py dataPre.py sen2IS_net.py img2map.py DataGenerator traValFiles_spatial traValFiles_random img2mapC recall_m modelSelection f1_m dice_coef precision_m sen2IS_net_bn sen2IS_net_bn_core sen2IS_net_deep sen2IS_net_bn_core_2 unet sen2IS_net_wide str arange print loadmat int... | # Use the trained model to predict HSE from S2 images ## Software Requirements environment.yml ## use demo - put the image data (s2) into folder: ./data/img/ - run python img2map.py - prediciton will be in folder ./data/pre/ ## reference data https://drive.google.com/drive/folders/1n2LGeGAv_O2cvxAJnSGNRUI4FMsm4psa?us... | 222 |
ClancyZhou/P_Net_Anomaly_Detection | ['anomaly detection'] | ['Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images'] | utils/gan_loss.py utils/parser.py utils/utils.py train_structure_extraction_network.py train_P_Net.py networks/unet.py networks/unet_part.py networks/discriminator.py networks/P_Net_v1.py dataloader/resc_dataloader.py utils/visualizer.py MultiTestForFigures RunMyModel PNetModel SegTransferModel RunMyModel RandomEnhance... | # P-Net for Anomaly Detection (Pytorch) This is the implementation of the paper: Kang Zhou, Yuting Xiao, Jianlong Yang, Jun Cheng, Wen Liu, Weixin Luo, Zaiwang Gu, Jiang Liu, Shenghua Gao. Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal Images. ECCV 2020. using PyTorch. **If you have any... | 223 |
ClorverCcy/GEDLoss_pytorch | ['speech synthesis'] | ['A Spectral Energy Distance for Parallel Speech Synthesis'] | spectral_ops.py torch_get_spectral_matrix torch_calc_spectrograms torch_hertz_to_mel torch_build_mel_basis torch_sum_spectral_dist torch_aligned_random_crop torch_matmul_real_with_complex torch_ged torch_mel_to_hertz shape torch_hertz_to_mel reshape view_as_complex torch_mel_to_hertz cos pi linspace sin float range mat... | # GEDLoss_pytorch a pytorch implementation of Google GEDLoss Full-text paper available on [arXiv](https://arxiv.org/abs/2008.01160). Origin code of TensorFlow edition at [GED_TTS](https://github.com/google-research/google-research/tree/68c738421186ce85339bfee16bf3ca2ea3ec16e4/ged_tts) | 224 |
CoNexDat/mw2v | ['word embeddings'] | ['Learning language variations in news corpora through differential embeddings'] | NYT-TG/SimiLab.py tempName testClass | # mw2v Folders: Each of the folders corresponds to a different dataset. * NYT: files for New York Times newspaper dataset. Slices are years, from 1990 to 2016. * TG: files for The Guardian newspaper dataset. Slices are years, from 1999 to 2016. * NYT-TG: files for the two-source model, where one slice is the NYT and t... | 225 |
CodeAchieveDream/crnn_model | ['optical character recognition', 'scene text recognition'] | ['An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition'] | tolmdb.py imgaug_image.py alphabet.py dataset.py crnn_test.py recog.py params.py net/crnn.py utils.py crnn_train.py val trainBatch init_args training weights_init lmdbDataset alignCollate randomSequentialSampler resizeNormalize generate_image generate CRNN_MODEL createDataset writeCache init_args checkImageIsValid to_a... | # Convolutional Recurrent Neural Network This software implements the Convolutional Recurrent Neural Network (CRNN), a combination of CNN, RNN and CTC loss for image-based sequence recognition tasks, such as scene text recognition and OCR. For details, please refer to our paper http://arxiv.org/abs/1507.05717. ### warp... | 226 |
Cold-Winter/Nattack | ['adversarial attack'] | ['NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks'] | cascade_adv_training/evaluation.py cascade_adv_training/resnet.py robustnet/plot_accu.py inputtransformations/robustml_model.py cascade_adv_training/utils.py inputtransformations/robustml_model_origin.py lid/evaluation_bpda.py robustnet/main.py inputtransformations/quilt_preprocess.py sap/helpers.py cascade_adv_trainin... | # NATTACK: A STRONG AND UNIVERSAL GAUSSIAN BLACK-BOX ADVERSARIAL ATTACK Data and model can be found here: [data and model](https://knightsucfedu39751-my.sharepoint.com/:f:/g/personal/liyandong_knights_ucf_edu/EnmkFaQkvwdDq0xcqIbbEfYBAhSkQK16ONPgjMJncbCwmg). Please download the data&&model and unzip them to './cifar-d... | 227 |
ColumbiaDVMM/Transform_Covariant_Detector | ['image retrieval'] | ['Learning discriminative and transformation covariant local feature detectors.', 'Learning Discriminative and Transformation Covariant Local Feature Detectors'] | tensorflow/patch_network_point_test.py tensorflow/patch_cnn.py eval/external/vlfeat-0.9.18/docsrc/wikidoc.py eval/external/vlfeat-0.9.18/docsrc/webdoc.py tensorflow/patch_network_train_point.py eval/external/vlfeat-0.9.18/docsrc/doxytag.py tensorflow/patch_reader.py eval/external/vlfeat-0.9.18/docsrc/mdoc.py eval/exter... | ## Learning Discriminative and Transformation Covariant Local Feature Detectors This code is the training and evaluation code for our CVPR 2017 paper. It includes the implement of a translation covariant local feature detector. The affine covariant model will be added in the future. @inproceedings{zhang2017learning, ... | 228 |
ConstantinSeibold/SGL | ['multiple instance learning'] | ['Self-Guided Multiple Instance Learning for Weakly Supervised Disease Classification and Localization in Chest Radiographs'] | MNIST-Bags_Experiments/mnist_exp.py MNIST-Bags_Experiments/bags_nets.py MNIST-Bags_Experiments/functions_.py MNIST-Bags_Experiments/mnist_bags.py F5 C1 C3 F4 LeNet5 C2 sgl cust_bce mean weight max MnistBags init_func get_loaders run_max run_mmm run_mean run_bil run_sgl run view mean cuda abs max view cust_bce sigmoid p... | # Self-guiding Loss for Multiple Instance Learning  The Self-Guiding Loss is a novel multiple-instance learning loss which integrates artificial supervision based on the networks predictions into its formulation in an online step. The SGL can be seen as an extension to the st... | 229 |
ContextScout/ned-graphs | ['entity disambiguation'] | ['Named Entity Disambiguation using Deep Learning on Graphs'] | wikidata_entity_linking_with_attentive_gcn/wikidata_query/sentence_processor.py wikidata_entity_linking_with_text_and_centroid/wikidata_query/utils.py wikidata_entity_linking_with_rnn_triplets/wikidata_query/reformat_wikidata.py wikidata_entity_linking_with_attentive_rnn_triplets/wikidata_query/graphs.py wikidata_entit... | Code and Dataset for Named Entity Disambiguation using Deep Learning on Graphs ============================================================================== This repository contains the code and dataset for the paper "Named Entity Disambiguation using Deep Learning on Graphs". The full paper can be found [here](https:... | 230 |
Coolgiserz/NLP_starter | ['text classification', 'stochastic optimization'] | ['Optimization Methods for Large-Scale Machine Learning', 'Convex Optimization: Algorithms and Complexity'] | CodePratices/MachineLearning/pos_tagging_hmm_project/main.py CodePratices/MachineLearning/pos_tagging_hmm_project/models/hmm.py CodePratices/MachineLearning/pos_tagging_hmm_project/utils/corpus.py test HMMPOSTagger CorpusHelper print decode | # README ## 学习资料 关于人工智能、机器学习、自然语言处理等领域的学习资料。 ### 课程 #### 人工智能 - [Artificial Intelligence: A Modern Approach](http://aima.cs.berkeley.edu) #### 机器学习 - 台大李宏毅机器学习课程： [中文课程！台大李宏毅机器学习公开课2019版上线](https://zhuanlan.zhihu.com/p/59655414) [[课程地址]](http://speech.ee.ntu.edu.tw/~tlkagk/courses_ML19.html) - [CS224W: Machine Learni... | 231 |
CrazySummerday/ctpn.pytorch | ['scene text detection'] | ['Detecting Text in Natural Image with Connectionist Text Proposal Network'] | train.py ctpn/dataset.py ctpn/config.py ctpn/utils.py ctpn/ctpn.py predict.py get_text_boxes save_checkpoint weights_init RPN_REGR_Loss CTPN_Model RPN_CLS_Loss basic_conv VOCDataset readxml ICDARDataset nms clip_bbox TextProposalConnectorOriented Graph bbox_transfrom filter_bbox compute_iou TextLineCfg cal_rpn TextProp... | # ctpn.pytorch Pytorch implementation of CTPN (Detecting Text in Natural Image with Connectionist Text Proposal Network) # Paper https://arxiv.org/pdf/1609.03605.pdf # train training dataset: ICDAR2013 and ICDAR2017. If you want to train your own dataset, you need to change the 'img_dir' and 'label_dir' in file *ctpn... | 232 |
Crespo-dong/caffe_ocr | ['optical character recognition', 'scene text recognition'] | ['An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition'] | examples/pycaffe/layers/pascal_multilabel_datalayers.py examples/finetune_flickr_style/assemble_data.py tools/extra/resize_and_crop_images.py tools/extra/summarize.py src/caffe/test/test_data/generate_sample_data.py examples/pycaffe/tools.py tools/extra/parse_log.py 3rdparty/include/google/protobuf/arena_nc_test.py exa... | # 简介 caffe_ocr是一个对现有主流ocr算法研究实验性的项目，目前实现了CNN+BLSTM+CTC的识别架构，并在数据准备、网络设计、调参等方面进行了诸多的实验。代码包含了对lstm、warp-ctc、multi-label等的适配和修改，还有基于inception、restnet、densenet的网络结构。代码是针对windows平台的，linux平台下只需要合并相关的修改到caffe代码中即可。 ## caffe代码修改 1. data layer增加了对multi-label的支持<br> 2. lstm使用的是junhyukoh实现的lstm版本（lstm_layer_Junhyuk.cpp/cu），原版... | 233 |
Cysu/person_reid | ['person re identification'] | ['Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification'] | data/format_3dpes.py tools/make_lists_id_training.py data/format_prid.py utils/__init__.py data/format_shinpuhkan.py data/format_cuhk03.py utils/core.py data/format_ilids.py tools/save_joint_impact_score.py tools/convert_lmdb_to_numpy.py tools/save_individual_impact_score.py utils/cmc.py eval/metric_learning.py data/fo... | # Domain Guided Dropout for Person Re-id This project aims at learning generic person re-identification (re-id) deep features from multiple datasets with domain guided dropout. Mainly based on our CVPR 2016 paper [Learning Deep Feature Representations with Domain Guided Dropout for Person Re-identification](http://www.... | 234 |
D-Roberts/lq_backprop | ['graph learning'] | ['QR and LQ Decomposition Matrix Backpropagation Algorithms for Square, Wide, and Deep -- Real or Complex -- Matrices and Their Software Implementation'] | test_lq_op_grad.py lq_op_grad.py lq LqGrad numeric_q test theoretic_q _extra_feeds _test_LQ_op numeric_l theoretic_l qr adjoint _LqGradSquareAndWideMatrices matmul update seed eps astype Session seed convert_to_tensor reshape astype range shape lq ravel prod Session seed convert_to_tensor reshape astype range shape lq ... | # LQ Matrix Backpropagation Algorithm Implementation TensorFlow implementation of differentiable LQ matrix decomposition for square, wide and deep tensors. # To Use Requirements: tf >v1; Python > 3.6. Recommended: install Anaconda. Create a tensorflow environment. ``` # tf cpu only; v2 by default at this time. conda c... | 235 |
D3-AI/Orion | ['anomaly detection'] | ['Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding'] | orion/benchmark.py orion/primitives/intervals.py orion/data.py tests/test_analysis.py orion/primitives/estimators.py orion/evaluation/point.py tests/test_benchmark.py orion/db/schema.py tutorials/tulog/utils.py orion/__main__.py orion/primitives/azure_anomaly_detector.py orion/core.py tests/test_functional.py tests/eva... | <p align="left"> <img width=15% src="https://dai.lids.mit.edu/wp-content/uploads/2018/06/Logo_DAI_highres.png" alt=“DAI-Lab” /> <i>An open source project from Data to AI Lab at MIT.</i> </p> <p align="left"> <img width=20% src="https://dai.lids.mit.edu/wp-content/uploads/2018/08/orion.png" alt=“Orion” /> </p> [![Develo... | 236 |
DCSong/CRNN-DenseNet | ['optical character recognition', 'scene text recognition'] | ['An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition'] | main_v2.py Config.py infer.py model_v2.py data_generator/generator.py data_generator/transform.py main.py infer_v2.py model.py dictionary.py cv_imread infer Idx2Word decode cv_imread infer Idx2Word decode valid decode_trueLabel minStringDistance calculate_accuracy ImageDataSet collate_fn ctc_greedy_decoder train valid ... | # CRNN-DenseNet a DenseNet-CRNN implement with PyTorch An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition https://arxiv.org/abs/1507.05717 ## 概况 CRNN是一种经典的用于文本/场景文本识别架构 这里我们用CRNN来进行印刷体文本行的识别 提供了CRNN的两种实现：原始版本的VGG实现和DenseNet作为CNN架构的实现(后缀v2) 文件夹结构和其他... | 237 |
DENG-MIT/CRNN | ['time series'] | ['Autonomous Discovery of Unknown Reaction Pathways from Data by Chemical Reaction Neural Network'] | HyChem/gen_data_pyrolysis.py | # CRNN (Chemical Reaction Neural Network) CRNN is an interpretable neural network architecture for autonomously inference chemical reaction pathways in various chemical systems. It is designed based on the following two fundamental physics laws: the Law of Mass Action and Arrhenius Law. It is also possible to incorpora... | 238 |
DFKI-NLP/REval | ['relation extraction'] | ['Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction'] | reval/probing_tasks/sent_length.py reval/probing_tasks/probing_task_base.py reval/engine.py reval/__init__.py probing_task_evaluation.py reval/probing_tasks/pos_tag_argument_position.py reval/probing_tasks/entity_exists_between_head_tail.py reval/probing_tasks/sdp_tree_depth.py reval/datasets.py reval.py reval/probing_... | # REval ## Table of Contents * [Introduction](#introduction) * [Overview](#-overview) * [Requirements](#-requirements) * [Installation](#-installation) * [Probing](#-probing) * [Usage](#-usage) * [Citation](#-citation) * [License](#-license) | 239 |
DFKI-NLP/RelEx | ['relation extraction'] | ['Probing Linguistic Features of Sentence-Level Representations in Neural Relation Extraction'] | relex/modules/seq2vec_encoders/bag_of_embeddings_encoder.py relex/predictors/__init__.py relex/modules/nn.py relex/dataset_readers/tacred.py relex/modules/seq2vec_encoders/utils.py tests/models/basic_relation_classifier_test.py relex/modules/offset_embedders/__init__.py relex/metrics/f1_measure.py relex/dataset_readers... | # RelEx A simple framework for Relation Extraction built on AllenNLP. --- ## 🔭 Overview | Path | Description | |------------------------- |------------------------------ | | [configs/](configs/) | This directory contains model configurations for relation classific... | 240 |
DFKI-NLP/TRE | ['relation extraction', 'unsupervised pre training'] | ['Improving Relation Extraction by Pre-trained Language Representations'] | logging_utils.py analysis_util.py train_utils.py utils.py datasets/__init__.py text_utils.py model_pytorch.py opt.py dataset_converter.py datasets/semeval_2010_task8.py relation_extraction.py loss.py read_experiment_logs experiments_to_dataframe evaluate_semeval2010_task8 read_log_file add_official_scorer_metrics load_... | # Improving Relation Extraction by Pre-trained Language Representations
This repository contains the code of our paper:
[Improving Relation Extraction by Pre-trained Language Representations.](https://openreview.net/forum?id=BJgrxbqp67)
Christoph Alt*, Marc Hübner*, Leonhard Hennig
We fine-tune the pre-trai... | 241 |
DFKI-NLP/tacrev | ['relation extraction', 'unsupervised pre training'] | ['TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task'] | tacrev/analysis/plotting.py tests/readers/evaluation_results_test.py tests/readers/tacred_test.py tests/__init__.py tacrev/analysis/errudite/utils.py tacrev/writers/writer_utils.py scripts/apply_tacred_patch.py tacrev/analysis/errudite/attributes.py tacrev/readers/tacred.py tacrev/readers/__init__.py tacrev/analysis/er... | # TACRED Revisited: A Thorough Evaluation of the TACRED Relation Extraction Task [[Paper](https://arxiv.org/abs/2004.14855)] ## Table of Contents * [Overview](#-overview) * [Requirements](#-requirements) * [Installation](#-installation) * [Patch TACRED](#-patch-the-original-tacred) * [Experiments](#-experiments) * [Cit... | 242 |
DIAGNijmegen/neural-odes-segmentation | ['medical image segmentation', 'semantic segmentation'] | ['Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands'] | model_utils.py metrics.py dataloader.py models.py train_utils.py augmentations.py inference_utils.py RandomRotationWithMask ElasticTransformations GLaSDataLoader postprocess split_objects remove_small_object inference_image hole_filling_per_object evaluate_image grow_to_fill_borders resize_image crop_result resize_to_s... | # Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands *Accepted to Medical Imaging meets NeurIPS workshop at NeurIPS 2019* Automated medical image segmentation plays a key role in quantitative research and diagnostics. Convolutional neural networks based on the U-Net architecture... | 243 |
DIAGNijmegen/pathology-streaming-pipeline | ['whole slide images', 'multiple instance learning'] | ['Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels'] | streaming/train.py streaming/train_remote.py streaming/torch_utils/streaming_trainer.py streaming/experiment_options.py streaming/tissue_dataset.py streaming/torch_utils/checkpointed_trainer.py streaming/torch_utils/utils.py streaming/torch_utils/diagnostics.py streaming/torch_utils/samplers.py streaming/torch_utils/sc... | Whole-slide classification pipeline — end-to-end ====== This repository will give an overview on how to use [streaming](https://github.com/DIAGNijmegen/StreamingCNN) to train whole slides to single labels. Streaming is an implementation of convolutions using tiling and gradient checkpointing to save memory. ![alt... | 244 |
DIAL-RPI/PIPO-FAN | ['semantic segmentation'] | ['Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction'] | pipo_fan/segment_sf_partial.py pipo_fan/dice.py pipo_fan/model/concave_dps_w.py pipo_fan/resample.py pipo_fan/dataset/dataset_liverCT_2D.py pipo_fan/dataset/dataset_muor_2D.py pipo_fan/model/concave_dps.py pipo_fan/model/denseu_net.py pipo_fan/train_concave0.py pipo_fan/model/resu_net.py pipo_fan/model/unet.py pipo_fan... | # Multi-organ Segmentation over Partially Labeled Datasets with Multi-scale Feature Abstraction ## Introduction In this paper, we propose a novel network architecture for unified multi-scale feature abstraction, which incorporates multi-scale features in a hierarchical fashion at various depths for image segmentation. ... | 245 |
DIVA-DIA/Text-Line-Segmentation-Method-for-Medieval-Manuscripts | ['denoising', 'semantic segmentation'] | ['Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts'] | src/line_segmentation/preprocessing/preprocess.py src/line_segmentation/utils/XMLhandler.py src/line_segmentation/utils/graph_util.py src/pixel_segmentation/evaluation/evaluate_algorithm.py src/line_segmentation/evaluation/overall_score_singel_eval.py src/line_segmentation/evaluation/overall_score.py src/line_segmentat... | **→ Link to paper: [https://arxiv.org/abs/1906.11894](https://arxiv.org/abs/1906.11894)** # Text Line Segmentation Method for Medieval Manuscripts Image and Text Segmentation pipeline for the paper ["Labeling, Cutting, Grouping: an Efficient Text Line Segmentation Method for Medieval Manuscripts"](https://arxiv.or... | 246 |
DLTK/DLTK | ['semantic segmentation'] | ['DLTK: State of the Art Reference Implementations for Deep Learning on Medical Images'] | dltk/core/upsample.py tests/test_sliding_window_segmentation.py dltk/io/abstract_reader.py dltk/networks/autoencoder/convolutional_autoencoder.py dltk/networks/segmentation/fcn.py data/IXI_Guys/download_IXI_Guys.py dltk/version.py dltk/core/residual_unit.py examples/applications/IXI_HH_superresolution/train.py dltk/cor... | ## Deep Learning Toolkit (DLTK) for Medical Imaging [](https://gitter.im/DLTK/DLTK?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) [](https://coveralls.io/github/DLTK/DLTK?bra... | 247 |
DNNToolBox/Net-Trim-v1 | ['network pruning'] | ['Fast Convex Pruning of Deep Neural Networks'] | main.py NetTrimSolver.py GeneralSoftmaxModel.py GeneralSoftmaxModel train_neural_network NetTrimSolver net_trim_solver_np get_weights initialize format compute_signals print train len labels GeneralSoftmaxModel images savemat zip compute_accuracy range read_data_sets count T reshape transpose maximum matmul solve_trian... | # Net-Trim (first version) This page contains the first version of Net-Trim, which addresses the **regularized form** of the Net-Trim convex program discussed in the [NIPS (2017) paper](https://papers.nips.cc/paper/6910-net-trim-convex-pruning-of-deep-neural-networks-with-performance-guarantee). See *main.py* for an ex... | 248 |
DTaoo/Discriminative-Sounding-Objects-Localization | ['object localization'] | ['Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching'] | music-exp/training_stage_two.py compared model/Sound-of-Pixels/dataset/music.py compared model/Sound-of-Pixels/create_index_files.py music-exp/eval_duet.py music-exp/data/syn_dataset.py compared model/Sound-of-Pixels/utils.py music-exp/model/location_model.py music-exp/training_stage_one.py compared model/Sound-of-Pixe... | # **Discriminative Sounding Objects Localization** Code for our NeurIPS 2020 paper [**Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching**](https://arxiv.org/abs/2010.05466) (The previous title is **Learning to Discriminatively Localize Sounding Objects in a Cocktail-party Scenario**)... | 249 |
DVLP-CMATERJU/Skip-Connected-Multi-column-Network | ['optical character recognition'] | ['A Skip-connected Multi-column Network for Isolated Handwritten Bangla Character and Digit recognition'] | Multi-Scale-Multi-Column-CNN/Other Datasets/Level_Wise_Concatenation/MSCNN_Level_Wise.py Multi-Scale-Multi-Column-CNN/Column_Wise_Concatenation/MSCNN_Column_Wise.py Multi-Scale-Multi-Column-CNN/All_Feature_Concatenation/Softmax-SVM.py Multi-Scale-Multi-Column-CNN/Level_Wise_Concatenation/MSCNN_Level_Wise.py Multi-Scale... | # Skip-Connected-Multi-column-Network | 250 |
Daftstone/Random-Directional-Attack | ['speech recognition'] | ['Random Directional Attack for Fooling Deep Neural Networks'] | cleverhans/scripts/plot_success_fail_curve.py cleverhans/cleverhans/canary.py cleverhans/tests_tf/test_mnist_tutorial_cw.py cleverhans/cleverhans/picklable_model.py cleverhans/cleverhans_tutorials/mnist_tutorial_picklable.py cleverhans/cleverhans/utils.py cleverhans/examples/multigpu_advtrain/test_run_multigpu.py cleve... | # Random Directional Attack for Fooling Deep Neural Networks This project is for the paper "Random Directional Attack for Fooling Deep Neural Networks". Our implementation is based on [cleverhans](https://github.com/tensorflow/cleverhans/tree/v.3.0.1) . The code was developed on Python 3.6 ## 1. Install dependencies. ... | 251 |
DagnyT/hardnet | ['image retrieval', 'patch matching'] | ["Working hard to know your neighbor's margins: Local descriptor learning loss"] | code/Losses.py code/download_all_datasets.py examples/extract_hardnet_desc_from_hpatches_file.py code/Loggers.py benchmarks/hpatches_extract_HardNet.py code/dataloaders/HPatchesDatasetCreator.py code/check_gor_triplet.py code/HardNet.py code/HardNetClassicalHardNegMiningSiftInit.py code/HardNetMultipleDatasets.py code/... | # HardNet model implementation HardNet model implementation in PyTorch for NIPS 2017 paper ["Working hard to know your neighbor's margins: Local descriptor learning loss"](https://arxiv.org/abs/1705.10872) [poster](http://cmp.felk.cvut.cz/~mishkdmy/posters/hardnet2017.pdf), [slides](http://cmp.felk.cvut.cz/~mishkdmy/sl... | 252 |
Dahee96/Seq2seq- | ['speech recognition', 'noisy speech recognition', 'distant speech recognition'] | ['The PyTorch-Kaldi Speech Recognition Toolkit'] | kaldi_decoding_scripts/utils/nnet/gen_hamm_mat.py kaldi_decoding_scripts/utils/reverse_arpa.py kaldi_decoding_scripts/utils/nnet/gen_splice.py kaldi_decoding_scripts/utils/nnet/gen_dct_mat.py kaldi_decoding_scripts/utils/filt.py kaldi_decoding_scripts/utils/nnet/make_nnet_proto.py kaldi_decoding_scripts/utils/nnet/make... | <<<<<<< HEAD # The PyTorch-Kaldi Speech Recognition Toolkit <img src="pytorch-kaldi_logo.png" width="220" img align="left"> PyTorch-Kaldi is an open-source repository for developing state-of-the-art DNN/HMM speech recognition systems. The DNN part is managed by PyTorch, while feature extraction, label computation, and ... | 253 |
Daikenan/ASRCF | ['visual tracking'] | ['Visual Tracking via Adaptive Spatially-Regularized Correlation Filters'] | external_libs/matconvnet/utils/proto/caffe_fastrcnn_pb2.py external_libs/matconvnet/doc/matdocparser.py external_libs/matconvnet/doc/matdoc.py external_libs/matconvnet/utils/proto/caffe_6e3916_pb2.py external_libs/matconvnet/utils/proto/caffe_pb2.py external_libs/matconvnet/utils/proto/caffe_b590f1d_pb2.py external_lib... | # ASRCF - Visual Tracking via Adaptive Spatially-Regularized Correlation Filters(**CVPR2019 Oral**). <div align="center"> <img src="https://github.com/Daikenan/ASRCF/blob/master/faceocc1.gif" width="500px" /> </div> ## abstract In this work, we propose a novel adaptive spatially-regularized correlation filters (ASR... | 254 |
DanailKoychev/neural-style | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | utils.py vgg16_nofc.py style_transfer.py loss_functions.py frobenious_norm gram_matrix style_transfer_loss content_loss style_loss parse_arguments load_image save_image trim_colors Vgg16 as_list reshape matmul content_loss style_loss subtract conv4_2 as_list frobenious_norm pack frobenious_norm conv3_3 conv2_2 subtract... | Neural Style =================== A simple implementaton of the style transfer algorithm described here: https://arxiv.org/abs/1508.06576 --- <img src="https://raw.githubusercontent.com/DanailKoychev/neural-style/master/sample-images/jf1.png" height="300px"> --- <p float="left"> <img src="https://raw.githubuserconte... | 255 |
Danial-Alh/fast-bleu | ['text generation'] | ['Jointly Measuring Diversity and Quality in Text Generation Models'] | old_metrics/bleu-old.py old_metrics/self_bleu.py old_metrics/bleu.py fast_bleu/__python_wrapper__.py fast_bleu/__init__.py fast_bleu/__test__.py setup.py test_cases.py test_cases/test_case2.py old_metrics/utils.py InstallCommand CleanCommand BuildExtWithoutPlatformSuffix nltk_bleu cpp_bleu nltk_self_bleu cpp_self_bleu ... | # fast-bleu Package This is a fast multithreaded C++ implementation of NLTK BLEU with Python wrapper; computing BLEU and SelfBLEU scores for a fixed reference set. It can return (Self)BLEU for different (max) n-grams simultaneously and efficiently (e.g. BLEU-2, BLEU-3, etc.). ## Installation The installation requires `... | 256 |
DanielTakeshi/gym-cloth | ['imitation learning'] | ['Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor'] | render/ext/nanogui/ext/pybind11/tests/test_smart_ptr.py gym_cloth/envs/__init__.py render/ext/nanogui/ext/pybind11/tools/clang/enumerations.py render/ext/nanogui/python/example1.py render/ext/nanogui/docs/exhale.py render/ext/nanogui/ext/pybind11/tests/test_issues.py render/ext/nanogui/ext/pybind11/tests/test_numpy_arr... | # Gym Cloth Quick logistics overview: this is *one* of the code bases used in our paper "Deep Imitation Learning of Sequential Fabric Smoothing From an Algorithmic Supervisor" with [arXiv here][3] and [project website here][4]. The arXiv version will have the most up-to-date version of the paper. <hr> This creates a g... | 257 |
DanieleAlessandro/KENN2 | ['multi label classification'] | ['Neural Networks Enhancement with Logical Knowledge'] | src/KENN2/layers/relational/Join.py src/KENN2/layers/residual/ClauseEnhancer.py src/KENN2/parsers.py src/KENN2/layers/Kenn.py src/KENN2/layers/residual/KnowledgeEnhancer.py src/KENN2/layers/RelationalKENN.py src/KENN2/layers/relational/GroupBy.py src/KENN2/layers/RangeConstraint.py relational_parser unary_parser unary_... | # KENN: Knowledge Enhanced Neural Networks KENN2 (Knowledge Enhanced Neural Networks 2.0) is a library for Python 3 built on top of TensorFlow 2 that allows you to modify neural network models by providing logical knowledge in the form of a set of universally quantified FOL clauses. It does so by adding a new final lay... | 258 |
DanieleGammelli/CensoredGP | ['gaussian processes'] | ['Estimating Latent Demand of Shared Mobility through Censored Gaussian Processes'] | GPy/likelihoods/loggaussian.py GPy/kern/src/psi_comp/linear_psi_comp.py GPy/core/parameterization/transformations.py GPy/kern/src/sde_matern.py GPy/plotting/matplot_dep/variational_plots.py GPy/core/parameterization/priors.py GPy/inference/latent_function_inference/var_dtc.py GPy/kern/src/standard_periodic.py GPy/kern/... | # Censored Gaussian Processes --------------------------------------------------------------- This repository is the official implementation of the CGP, from *Estimating latent demand of shared mobility through censored Gaussian Processes*. The full paper is available here: [link1](https://arxiv.org/abs/2001.07402), [l... | 259 |
DanieleGammelli/multi-output-gp-censored-regression | ['stochastic optimization'] | ['Generalized Multi-Output Gaussian Process Censored Regression'] | models.py distributions.py likelihoods.py PyroCensoredNegBinomial PyroCensoredPoison PyroNegBinomial PyroCensoredNormal Poisson CensoredNegBinomial CensoredHomoscedGaussian CensoredPoisson Gaussian CensoredHeteroscedGaussian NegBinomial HeteroscedVariationalGP VariationalGP real nonnegative_integer nonnegative_integer ... | # Generalized Multi-Output Gaussian Process Censored Regression --------------------------------------------------------------- This repository is the official implementation of the HMOCGP, from *Generalized Multi-Output Gaussian Process Censored Regression*. The full paper is available [here](https://arxiv.org/abs/200... | 260 |
Daniil-Selikhanovych/Shampoo_optimizer | ['stochastic optimization'] | ['Shampoo: Preconditioned Stochastic Tensor Optimization'] | matrix_square_root_power.py shampoo_optimizer.py matrix_square_root matrix_inverse_pth_root ShampooOptimizer GetParam while_loop reduce_sum sqrt cast int32 eye norm while_loop pow cast int32 eye callable | # Shampoo_optimizer Our implementation of Shampoo optimizer based on https://arxiv.org/pdf/1802.09568.pdf. It consists of different notebooks, which we used on our own computers or Google Colab. Use scripts matrix_square_root_power.py and shampoo_optimizer.py both for using our code. Method `apply_gradients` does one i... | 261 |
DanyWind/fastai_bs_finder | ['dota 2'] | ['An Empirical Model of Large-Batch Training'] | bs_finder.py bs_find BSFinder mom3 lin_comb get_flatten_grad parameters list cat lin_comb train_dl int BSFinder batch_size fit ceil train_ds len | DanyWind/fastai_bs_finder | 262 |
DaoD/ConstraintGraph4NSO | ['sentence ordering'] | ['Neural Sentence Ordering Based on Constraint Graphs'] | FirstPhase/prepare_data.py SecondPhase/Dataset.py SecondPhase/Evaluate.py SecondPhase/run.py FirstPhase/model.py SecondPhase/GINPointer.py SecondPhase/prepare_data.py SecondPhase/NeuralNetwork.py SecondPhase/Model.py set_seed evaluate evaluate_test PairProcessor MyTestDataset main train load_and_cache_examples main Dat... | DaoD/ConstraintGraph4NSO | 263 |
Darg-Iztech/gender-prediction-from-tweets | ['gender prediction'] | ['Gender Prediction from Tweets: Improving Neural Representations with Hand-Crafted Features'] | train.py visualizer.py modelDeleter.py eval.py parameters_es.py test.py parameters.py preprocess.py parameters_en.py main.py parameters_ar.py model.py test network flags flags flags flags readCaptions prepCharBatchData prepWordBatchData_tweet readData prepWordBatchData readGloveEmbeddings partite_dataset_vectors user2t... | # RNN and Captioning for Gender Classification Gender Classification From Tweets and Posted Images ## Requirements - Python 2.7 (it may work on python3 as well, not guaranteed) - (Preferable) CUDA 9.0 and Nvidia GPU compatible with CUDA - Word embeddings, we prefer GLoVe (https://nlp.stanford.edu/projects/glove/) - As... | 264 |
Darthholi/DocumentConcepts | ['table detection'] | ['Table understanding in structured documents'] | concepts_fixed.py generators.py concepts.py run_experiments.py concepts_rendered.py attention.py distributions.py utils.py boxgeometry.py concepts_test.py GatherFromIndices SinCosPositionalEmbedding AttentionTransformer BoundingBox get_items_reading_layout range_distance_ordered analyze_bboxes_positions range_overlap_s... | #### Simulating invoice generation process This repository displays a process we went through to create a simulated business documents for fast experimentation. All experiments could be run as `python run_experiments.py command_name`, where command name is the name of the experiment. Simplest experiments are provided... | 265 |
Darthholi/similarity-models | ['one shot learning', 'table detection'] | ['Table understanding in structured documents', 'Learning from similarity and information extraction from structured documents'] | utils/sqlite_experiment_generator.py utils/__init__.py ds_local_sqlite.py utils/keras_utils_crop_render.py utils/manipulations_utils.py utils/textutils.py utils/k_utils.py experiments_reuse.py utils/dataflows.py experiments_ft.py utils/boxgeometry.py utils/attention.py experiments_ftreuse.py experiments_copy.py experim... | # Similarity models Similarity and data extraction Source codes to accompany the following publications: - https://ieeexplore.ieee.org/document/8892877 (older manuscript: https://arxiv.org/abs/1904.12577) - https://rdcu.be/cmoAk (or manuscript here: https://arxiv.org/abs/2011.07964) Dataset is hosted under kaggle data... | 266 |
DataMining-ClusteringAnalysis/CRAD-Clustering | ['time series', 'time series clustering'] | ['CRAD: Clustering with Robust Autocuts and Depth'] | CreateDistanceMatrix.py ExtensionToDBSCAN.py CRAD.py clustering_ _hist_find_new cal_adjM_cutOff dfs dbscan_newM _expand_cluster _region_query_cutOff arange tolist histogram append float array range len _hist_find_new remove extend append array range range len range dfs _hist_find_new remove DataFrame array append len r... | ## CRAD-Clustering Python implementation of CRAD clustering algorithm (CRAD.py) and extended DBSCAN algorithm using CRAD framework (ExtensionToDBSCAN.py). ## Setup `python setup.py install` ## Documentation For CRAD-Clustering: Call the function `cal_adjM_cutOff(xxDist, StepSize, Nbin)` to calculate adjancey matrix whe... | 267 |
Daulbaev/IRDM | ['density estimation'] | ['Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs'] | experiments/density/vae_lib/utils/plotting.py experiments/density/vae_lib/models/flows.py interpolated_torchdiffeq/_impl/rk_common.py experiments/density/vae_lib/utils/load_data.py interpolated_torchdiffeq/_impl/odeint_interpolated.py interpolated_torchdiffeq/_impl/spline.py experiments/density/vae_lib/utils/visual_eva... | # About Code for reproducing the experiments in the paper: > Daulbaev, T., Katrutsa, A., Markeeva, L., Gusak, J., Cichocki, A., & Oseledets, I. (2020). Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs. Advances in Neural Information Processing Systems, 33. > [[arxiv]](https://arxiv.org/abs/2003.... | 268 |
Davidham3/ASTGCN | ['traffic prediction'] | ['Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting'] | train.py test/test_data_preperation.py lib/data_preparation.py test/test_utils.py lib/utils.py test/test_metrics.py test/test_model.py model/mstgcn.py model/astgcn.py model/model_config.py lib/metrics.py MyInit normalization read_and_generate_dataset mean_absolute_error mean_squared_error masked_mape_np cheb_polynomial... | # ASTGCN
Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting (ASTGCN)
# References
[Shengnan Guo, Youfang Lin, Ning Feng, Chao Song, Huaiyu Wan(*). Attention Based Spatial-Temporal Graph Convolutional Networks for Traffic Flow F... | 269 |
Davidham3/STSGCN | ['traffic prediction'] | ['Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting'] | models/stsgcn.py test/test_stsgcn.py load_params.py utils.py main.py training construct_model generate_data mask_np construct_adj masked_mse_np masked_mae_np generate_seq generate_from_train_val_test get_adjacency_matrix masked_mape_np generate_from_data sthgcn_layer_sharing output_layer huber_loss weighted_loss stsgcn... | # STSGCN AAAI 2020. Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting url: paper/AAAI2020-STSGCN.pdf # Usage Docker is recommended. 1. install docker 2. install nvidia-docker 3. build image using `cd docker && docker build -t stsgcn/mxnet_1.41_cu100... | 270 |
Davidnh8/artAI | ['style transfer'] | ['A Neural Algorithm of Artistic Style'] | Artist.py save_vgg19_weights_notop.py customvgg19_notop gramMatrix styleLossSingleLayer Artist contentLoss sum square dot transpose shape int gramMatrix Model Input load_weights | # AI Art This repo implements the art style transfer algorithm from "A Neural Algorithm of Artistic Style" (https://arxiv.org/abs/1508.06576) by Leon A with few modifications. Artist.py requires two base images, one for style and one for content. Then it extracts style and content respectively and merge the two to pro... | 271 |
Davidzhangyuanhan/CelebA-Spoof | ['face anti spoofing'] | ['CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations', 'CelebA-Spoof Challenge 2020 on Face Anti-Spoofing: Methods and Results'] | intra_dataset_code/tsn_predict.py intra_dataset_code/models.py intra_dataset_code/main.py intra_dataset_code/client.py intra_dataset_code/detector.py get_image verify_output get_tpr_from_threshold read_image get_thresholdtable_from_fpr CelebASpoofDetector run_test conv3x3 BasicBlock AENet Bottleneck TSNPredictor pretra... | # **CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations**  **CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich Annotations** [Yuanhan Zhang](https://github.com/Davidzhangyuanhan/CelebA-Spoof), [Zhenfei Yin](https://github.com/yinzhenfei), [Yid... | 272 |
DePengW/PSENet | ['optical character recognition', 'scene text detection', 'curved text detection'] | ['Shape Robust Text Detection with Progressive Scale Expansion Network', 'Shape Robust Text Detection with Progressive Scale Expansion Network'] | util/statistic.py util/tf.py pypse.py util/event.py train_ic15.py metrics.py util/feature.py util/proc.py test_ctw1500.py util/rand.py util/t.py util/neighbour.py util/caffe_.py models/__init__.py util/dtype.py dataset/icdar2015_loader.py util/ml.py util/str_.py util/test.py util/log.py test_ic15.py pse/.ycm_extra_conf... | # Shape Robust Text Detection with Progressive Scale Expansion Network ## Requirements * Python 2.7 * PyTorch v0.4.1+ * pyclipper * Polygon2 * OpenCV 3.4 (for c++ version pse) * opencv-python 3.4 ## Introduction Progressive Scale Expansion Network (PSENet) is a text detector which is able to well detect the arbitrary-s... | 273 |
DeanChan/NAE4PS | ['person search', 'person re identification', 'human detection'] | ['Norm-Aware Embedding for Efficient Person Search'] | configs/res50_faster_rcnn.py lib/datasets/cuhk_sysu.py configs/__init__.py lib/datasets/ps_dataset.py lib/utils/serialization.py lib/utils/distributed.py lib/datasets/__init__.py lib/datasets/prw.py lib/loss/__init__.py lib/utils/trainer.py scripts/train_NAE.py lib/utils/misc.py lib/loss/oim.py lib/model/faster_rcnn_pi... | # Norm-Aware Embedding for Efficient Person Search This repository hosts our code for our paper [Norm-Aware Embedding for Efficient Person Search](http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_Norm-Aware_Embedding_for_Efficient_Person_Search_CVPR_2020_paper.pdf). ## Preparation 1. Clone this repo ```bash... | 274 |
Deep-Imaging-Group/RED-WGAN | ['denoising'] | ['Denoising of 3-D Magnetic Resonance Images Using a Residual Encoder-Decoder Wasserstein Generative Adversarial Network'] | preprocessing.py model7.py data.py mixdata.py model2.py model5.py MRIDataset MRIValidDataset add_rice_noise MRIDataset MRIValidDataset add_rice_noise initialize_weights Net CNN3D initialize_weights WGAN GeneratorNet DiscriminatorNet VGG19 initialize_weights WGAN GeneratorNet DiscriminatorNet VGG19 merge_test_img add_ri... | Deep-Imaging-Group/RED-WGAN | 275 |
DeepLearnXMU/NSEG | ['sentence ordering'] | ['Graph-based Neural Sentence Ordering'] | data/data.py main.py model/seq2seq.py model/generator.py override parse_args set_seeds curtime MyBatch DocIter ParallelDataset NormalField DocField GraphField DocDataset Beam decode valid_model SGRU train beam_search_pointer print_params PointerNet GRNGOB GRUCell add_argument ArgumentParser seed manual_seed_all manual_... | Graph based Neural Sentence Ordering ===================================================================== ### Installation The following packages are needed: - Python == 3.6 - Pytorch >= 1.0 - torchtext == 0.3 - Stanford POS tagger or Dependency Parser - Glove (100 dim) ### Dataset Format | 276 |
DeepPathology/SlideRunner | ['whole slide images'] | ['SlideRunner - A Tool for Massive Cell Annotations in Whole Slide Images'] | SlideRunner/plugins/proc_macenko.py SlideRunner/plugins/proc_countdown.py SlideRunner/plugins/proc_ppc.py SlideRunner/plugins/proc_HPF.py SlideRunner/gui/style.py SlideRunner/gui/dialogs/dbmanager.py SlideRunner/plugins/proc_ObjDetResults.py SlideRunner/__main__.py SlideRunner/plugins/proc_secondaryDatabase.py SlideRun... |  # SlideRunner [](https://doi.org/10.1007/978-3-662-56537-7_81) *SlideRunner* is a tool for massive cell annotations in whole slide images. It has been created in close cooperation be... | 277 |
DeepSceneSeg/EfficientPS | ['panoptic segmentation', 'instance segmentation', 'semantic segmentation'] | ['EfficientPS: Efficient Panoptic Segmentation'] | mmdet/models/roi_extractors/__init__.py mmdet/ops/roi_sampling/__init__.py mmdet/__init__.py mmdet/datasets/pipelines/instaboost.py tools/cityscapes_save_predictions.py mmdet/ops/masked_conv/__init__.py mmdet/ops/norm.py mmdet/core/mask/utils.py mmdet/core/bbox/bbox_target.py mmdet/core/bbox/samplers/pseudo_sampler.py ... | # EfficientPS: Efficient Panoptic Segmentation [](https://paperswithcode.com/sota/panoptic-segmentation-on-cityscapes-val?p=efficientps-efficient-panoptic-s... | 278 |
DevashishPrasad/Smart-Traffic-Junction | ['density estimation'] | ['HOG, LBP and SVM based Traffic Density Estimation at Intersection'] | image-processing/main.py saverois.py save_HOG_LBP.py predictor.py classifier.py distMap sqrt uint8 float32 | # Smart-Traffic-Junction This repository contains the working implementation of our research paper [link](https://arxiv.org/abs/2005.01770). The paper was presented and published at IEEE PuneCon 19 conference. We propose a simple algorithm for traffic density estimation using image processing and machine learning. > **... | 279 |
Dictanova/term-eval | ['word embeddings'] | ['Towards a unified framework for bilingual terminology extraction of single-word and multi-word terms'] | term_eval.py precision_ranges rank_results max_precision read_gold_standard main read_result precision_ranges rank_results format print add_argument gold_standard max_precision ArgumentParser read_gold_standard parse_args float sum read_result result_file len defaultdict items sorted defaultdict list tolist reverse app... | Dictanova/term-eval | 280 |
Diooooo/Reimplement-HED | ['boundary detection', 'edge detection'] | ['Holistically-Nested Edge Detection'] | main.py hed.py data_parser.py read_file_lst randomize split_pair_names DataParser _to_tensor side_branch hed cross_entropy_balanced fuse_pixel_error generator train generate_edge readlines close open convert_to_tensor weighted_cross_entropy_with_logits base_dtype _to_tensor float32 log reduce_sum reduce_mean cast clip_... | # Reimplement-HED Reimplement Holistically-Nested Edge Detection(HED) using Keras Reference: https://github.com/lc82111/Keras_HED <br> The paper ***Holistically-Nested Edge Detection*** can be found here: https://arxiv.org/abs/1504.06375 | 281 |
DirtyHarryLYL/Transferable-Interactiveness-Network | ['human object interaction detection'] | ['Transferable Interactiveness Knowledge for Human-Object Interaction Detection', 'Transferable Interactiveness Knowledge for Human-Object Interaction Detection'] | lib/ult/config.py lib/ult/config_vcoco.py lib/models/test_VCOCO_D_pose_pattern_naked.py tools/Train_TIN_VCOCO.py script/Download_data.py lib/networks/TIN_VCOCO.py HICO-DET_Benchmark/config.py lib/ult/visualization.py tools/Train_TIN_HICO.py lib/models/test_HICO_pose_pattern_all_wise_pair.py HICO-DET_Benchmark/HICO_Benc... | # TIN: Transferable Interactiveness Network #### **News**: (2022.12.19) HAKE 2.0 is accepted by TPAMI! (2022.11.19) We release the interactive object bounding boxes & classes in the interactions within AVA dataset (2.1 & 2.2)! [HAKE-AVA](https://github.com/DirtyHarryLYL/HAKE-AVA), [[Paper]](https://arxiv.org/abs/221... | 282 |
DnanaDev/CRNN_for_OCR | ['optical character recognition', 'scene text recognition'] | ['An End-to-End Trainable Neural Network for Image-based Sequence Recognition and Its Application to Scene Text Recognition'] | CRNN Model_keras.py CRNN Model_keras_validation.py encode_to_labels ctc_lambda_func LossHistory encode_to_labels ctc_lambda_func append index enumerate | # A-CRNN-model-for-Text-Recognition-in-Keras To understand the algorithm used in the model follow these blogs: 1. https://theailearner.com/2019/05/29/creating-a-crnn-model-to-recognize-text-in-an-image-part-1/ 2. https://theailearner.com/2019/05/29/creating-a-crnn-model-to-recognize-text-in-an-image-part-2/ # Implement... | 283 |
Dodiom/dodiom | ['machine translation'] | ['Gamified Crowdsourcing for Idiom Corpora Construction'] | src/bot/handlers/feedback.py src/nlp/turkish/translation.py src/bot/handlers/start.py src/api/review.py src/bot/helpers/keyboard_helper.py src/nlp/english/parser.py src/nlp/italian/parser.py src/models.py src/bot/handlers/help.py src/bot/handlers/review.py src/bot/helpers/feedback_helper.py src/nlp/turkish/parser.py sr... | # Dodiom Code for the Telegram bot [Dodiom](https://t.me/mwetest_bot). ## Setup Create a new bot using [BotFather](https://t.me/botfather), get its token and put it in `docker/english/docker-compose.yml`, also put "1" for moderator id there (you can change this with your own Telegram ID after you find it) and then run ... | 284 |
Dong-JinKim/ActionCooccurrencePriors | ['human object interaction detection'] | ['Detecting Human-Object Interactions with Action Co-occurrence Priors', 'ACP++: Action Co-occurrence Priors for Human-Object Interaction Detection', 'Detecting Human-Object Interactions with Action Co-occurrence Priors'] | find_correlation.py utils/io.py exp/hoi_classifier/data/box_features.py data/hico/split_ids.py exp/hoi_classifier/data/assign_pose_to_human_candidates.py exp/hico_eval/sample_complexity_analysis.py data/hico/hoi_cls_count.py exp/hoi_classifier/vis/top_boxes_per_hoi.py exp/hoi_classifier/vis/faster_rcnn_aps.py exp/hoi_c... | # Action Co-occurrence Priors for HOI Detection Official code for our ECCV 2020 paper, **[Detecting Human-Object Interactions with Action Co-occurrence Priors](https://sites.google.com/view/action-cooccurrence)**. Done by Dong-Jin Kim, Xiao Sun, Jinsoo Choi, Stephen Lin, and In So Kweon. We Introduce novel "action co-o... | 285 |
DongHande/MCGL | ['graph learning', 'data augmentation'] | ['Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning'] | plot/comparison_clean.py plot/comparison_noisy.py train_GCN.py train_MC_base.py plot/deep_GCO.py ps.py models.py layers.py data/coauthorship/sparsegraph.py utils.py data/coauthorship/io.py Avelayer Conlayer Linear graph_ave GCN MLP parse_args train test train test get_noise_rate load_data_ms_academic sparse_mx_to_torch... | # Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning
This repository is the official implementation of [Data Augmentation View on Graph Convolutional Network and the Proposal of Monte Carlo Graph Learning](https://arxiv.org/abs/2006.13090).
## Overview
In this ... | 286 |
DongjuSin/computer-vision-proj | ['semantic segmentation'] | ['FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'] | encoding/parallel.py encoding/dilated/resnet.py encoding/utils/metrics.py encoding/functions/encoding.py encoding/models/encnet.py encoding/nn/syncbn.py scripts/prepare_pcontext.py encoding/models/psp.py encoding/functions/__init__.py encoding/models/model_store.py encoding/datasets/__init__.py encoding/nn/__init__.py ... | # FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation [[Project]](http://wuhuikai.me/FastFCNProject/) [[Paper]](http://wuhuikai.me/FastFCNProject/fast_fcn.pdf) [[arXiv]](https://arxiv.org/abs/1903.11816) [[Home]](http://wuhuikai.me) [. Some of them are described in the following paper: [Supervised Online Diarization with Sample Mean Loss for Multi-Domain Data](https://arxiv.org/abs/1911.012... | 288 |
DoodleJZ/ParsingAll | ['semantic parsing'] | ['Parsing All: Syntax and Semantics, Dependencies and Spans'] | src/Evaluator/dep_eval.py srl_scripts/filter_conll2012_data.py srl_scripts/cache_elmo.py src/pretrained_bert/tokenization.py src/Decoder/uniform_decoder.py src/utils_io.py src/Evaluator/conll09_utils.py src/Datareader/srlspan_reader.py src/transliterate.py src/pretrained_bert/file_utils.py srl_scripts/conll05_to_json.p... | # ParsingAll ## Contents 1. [Requirements](#Requirements) 2. [Training](#Training) ## Requirements * Python 3.6 or higher. * Cython 0.25.2 or any compatible version. * [PyTorch](http://pytorch.org/) 1.0.0+. * [EVALB](http://nlp.cs.nyu.edu/evalb/). Before starting, run `make` inside the `EVALB/` directory to compile an... | 289 |
DoubleML/doubleml-for-py | ['causal inference'] | ['Double/Debiased Machine Learning for Treatment and Causal Parameters'] | doubleml/tests/test_pliv.py doubleml/tests/test_irm_no_cross_fit.py doubleml/tests/test_plr_set_smpls_externally.py doubleml/_utils.py doubleml/tests/test_pliv_partial_x.py doubleml/tests/_utils_dml_cv_predict.py doubleml/tests/test_doubleml_set_sample_splitting.py doubleml/tests/test_pliv_partial_xz_tune.py doubleml/t... | # DoubleML - Double Machine Learning in Python <a href="https://docs.doubleml.org"><img src="https://raw.githubusercontent.com/DoubleML/doubleml-for-py/main/doc/logo.png" align="right" width = "120" /></a> [](https://github.com/DoubleML/doub... | 290 |
Doyosae/Deep_Complex_UNet | ['speech enhancement'] | ['Phase-aware Speech Enhancement with Deep Complex U-Net'] | model_module.py model_train.py model_test.py model_data.py complex_layers/activations.py complex_layers/__init__.py model_loss.py complex_layers/layer.py complex_layers/normalization.py complex_layers/STFT.py complex_layers/dcunet.py datagenerator weighted_SDR_loss modified_SDR_loss convariance_encoder_module decoder_m... | # Introuduction Impelmentation Phase-aware Speech Enhnacement Deep Complex UNet This is convolution neural networks model for Speech Enhancement Papers URL 1. [Phase-aware Speech Enhancement Deep Complex UNet - openreview](https://openreview.net/pdf?id=SkeRTsAcYm) 2. [Phase-aware Speech Enhancement Deep Complex... | 291 |
DreamInvoker/GAIN | ['relation extraction'] | ['Double Graph Based Reasoning for Document-level Relation Extraction'] | code/utils.py code/test.py code/data.py code/models/GAIN.py code/models/GAIN_nomention.py code/train.py code/config.py get_opt BERTDGLREDataset DGLREDataloader DGLREDataset test train Accuracy print_params get_cuda logging RelGraphConvLayer GAIN_BERT BiLSTM GAIN_GloVe Bert RelEdgeLayer Attention RelGraphConvLayer GAIN_... | # Double Graph Based Reasoning for Document-level Relation Extraction Source code for EMNLP 2020 paper: [Double Graph Based Reasoning for Document-level Relation Extraction](https://arxiv.org/abs/2009.13752) > Document-level relation extraction aims to extract relations among entities within a document. Different from ... | 292 |
DreamtaleCore/Refool | ['backdoor attack'] | ['Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks'] | train.py utils/__init__.py scripts/compute_each_class_attack_rate.py models/alexnet.py data.py models/wrn.py eval.py models/resnext.py test.py trainer.py models/vgg.py models/__init__.py models/densenet.py models/preresnet.py strategy.py scripts/insert_reflection.py models/resnet.py models/lenet5.py ImageLabelFilelist ... | # Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks     ![CUDA 10.0](https://img.shields.io/badge/cuda-10.0-DodgerBlue.svg?st... | 294 |
E666GT/TrafficPredictionNN | ['traffic prediction'] | ['Graph WaveNet for Deep Spatial-Temporal Graph Modeling'] | train.py test.py util.py engine.py train_test.py Test/print_test.py model.py Test/test_time.py trainer linear gcn nconv gwnet main main main load_pickle calculate_scaled_laplacian calculate_normalized_laplacian load_adj masked_mae metric masked_rmse asym_adj DataLoader masked_mape sym_adj load_dataset StandardScaler ma... | # Graph WaveNet for Deep Spatial-Temporal Graph Modeling ## Updating Log ### Variables sensor_ids, len=207, cont_sample="773869", a random 6-digit number\ adj_mx, shape=207,207 , if Identity, it is a eye(207)\ scaler, a variable maybe used in the later part to scale paras. It includes mean and std of the data sensor_id... | 295 |
EBjerrum/Deep-Chemometrics | ['data augmentation'] | ['Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics'] | ChemUtils.py EmscScaler dataaugment GlobalStandardScaler SavGolFilt float random range array | # Deep Chemometrics with Data Augmentation  The repository contains two examples of using convolutional neural networks to model spectroscopical data with data augmentation or EMSC as means to handle the variation in ... | 296 |
ECNU-ICA/ECNU-SenseMaker | ['graph attention', 'data augmentation'] | ['ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation'] | optimizer.py utils/getGraphUtils.py utils/text_to_uri.py loss.py utils/gpu_mem_track.py utils/commonsenseQAutils.py SemEval2020-Task4-Commonsense-Validation-and-Explanation-master/evaluation tools/taskB_scorer.py functions.py run_single_model.py run_ensemble_model.py SemEval2020-Task4-Commonsense-Validation-and-Explana... | # ECNU-SenseMaker (SemEval-2020 Task 4) <img src="https://upload.wikimedia.org/wikipedia/commons/thumb/9/96/Pytorch_logo.png/800px-Pytorch_logo.png" width="10%">[](https://raw.githubusercontent.com/im0qianqian/CodeforcesEduHacking/master/LICENSE) Sourc... | 297 |
EEEGUI/PSPNet | ['scene parsing', 'semantic segmentation'] | ['Semantic Understanding of Scenes through the ADE20K Dataset'] | train.py network.py tools.py inference.py evaluate.py image_reader.py model.py video.py load main get_arguments read_images_from_disk ImageReader image_scaling read_labeled_image_list random_crop_and_pad_image_and_labels image_mirroring load main get_arguments save PSPNet50 PSPNet101 layer Network load_img decode_label... | # PSPNet_tensorflow ## Introduction This is an implementation of PSPNet in TensorFlow for semantic segmentation on the [cityscapes](https://www.cityscapes-dataset.com/) dataset. We first convert weight from [Original Code](https://github.com/hszhao/PSPNet) by using [caffe-tensorflow](https://github.com/ethereon/caffe... | 298 |
EKirschbaum/DISCo | ['cell segmentation', 'instance segmentation', 'semantic segmentation'] | ['DISCo: Deep learning, Instance Segmentation, and Correlations for cell segmentation in calcium imaging'] | get_affs.py train.py run.py convert_results.py dataloading.py get_segmentation.py model.py combine_results convert_single_result DISCoDataset GetAffs get_segmentation get_mask BackgroundLabelSuperpixelGenerator DISCoNet Train_DISCoNet list File close where unique zip append keys list File close where unique zip append ... | # DISCo: Deep learning, Instance Segmentation and Correlations for cell segmentation in calcium imaging This is a method to perform the cell segmentaiton step in caclium imaging analysis, which uses the temporal information from caclium imaging videos in form of correlations, and combines a deep learning model with an ... | 299 |
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