uid int64 4 318k | paper_url stringlengths 39 81 | arxiv_id stringlengths 9 16 ⌀ | title stringlengths 6 365 | abstract stringlengths 0 7.27k | url_abs stringlengths 17 601 | url_pdf stringlengths 21 819 | proceeding stringlengths 7 1.03k ⌀ | authors sequence | tasks sequence | date float64 422B 1,672B ⌀ | methods list | __index_level_0__ int64 1 197k |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
172,339 | https://paperswithcode.com/paper/maximum-a-posteriori-signal-recovery-for | 2010.15682 | Maximum a posteriori signal recovery for optical coherence tomography angiography image generation and denoising | Optical coherence tomography angiography (OCTA) is a novel and clinically promising imaging modality to image retinal and sub-retinal vasculature. Based on repeated optical coherence tomography (OCT) scans, intensity changes are observed over time and used to compute OCTA image data. OCTA data are prone to noise and artifacts caused by variations in flow speed and patient movement. We propose a novel iterative maximum a posteriori signal recovery algorithm in order to generate OCTA volumes with reduced noise and increased image quality. This algorithm is based on previous work on probabilistic OCTA signal models and maximum likelihood estimates. Reconstruction results using total variation minimization and wavelet shrinkage for regularization were compared against an OCTA ground truth volume, merged from six co-registered single OCTA volumes. The results show a significant improvement in peak signal-to-noise ratio and structural similarity. The presented algorithm brings together OCTA image generation and Bayesian statistics and can be developed into new OCTA image generation and denoising algorithms. | https://arxiv.org/abs/2010.15682v1 | https://arxiv.org/pdf/2010.15682v1.pdf | null | [
"Lennart Husvogt",
"Stefan B. Ploner",
"Siyu Chen",
"Daniel Stromer",
"Julia Schottenhamml",
"A. Yasin Alibhai",
"Eric Moult",
"Nadia K. Waheed",
"James G. Fujimoto",
"Andreas Maier"
] | [
"Denoising",
"Image Generation"
] | 1,603,929,600,000 | [] | 25,324 |
3,841 | https://paperswithcode.com/paper/code-completion-with-neural-attention-and | 1711.09573 | Code Completion with Neural Attention and Pointer Networks | Intelligent code completion has become an essential research task to
accelerate modern software development. To facilitate effective code completion
for dynamically-typed programming languages, we apply neural language models by
learning from large codebases, and develop a tailored attention mechanism for
code completion. However, standard neural language models even with attention
mechanism cannot correctly predict the out-of-vocabulary (OoV) words that
restrict the code completion performance. In this paper, inspired by the
prevalence of locally repeated terms in program source code, and the recently
proposed pointer copy mechanism, we propose a pointer mixture network for
better predicting OoV words in code completion. Based on the context, the
pointer mixture network learns to either generate a within-vocabulary word
through an RNN component, or regenerate an OoV word from local context through
a pointer component. Experiments on two benchmarked datasets demonstrate the
effectiveness of our attention mechanism and pointer mixture network on the
code completion task. | http://arxiv.org/abs/1711.09573v2 | http://arxiv.org/pdf/1711.09573v2.pdf | null | [
"Jian Li",
"Yue Wang",
"Michael R. Lyu",
"Irwin King"
] | [
"Code Completion"
] | 1,511,740,800,000 | [] | 140,067 |
151,672 | https://paperswithcode.com/paper/naist-s-machine-translation-systems-for-iwslt | null | NAIST's Machine Translation Systems for IWSLT 2020 Conversational Speech Translation Task | This paper describes NAIST{'}s NMT system submitted to the IWSLT 2020 conversational speech translation task. We focus on the translation disfluent speech transcripts that include ASR errors and non-grammatical utterances. We tried a domain adaptation method by transferring the styles of out-of-domain data (United Nations Parallel Corpus) to be like in-domain data (Fisher transcripts). Our system results showed that the NMT model with domain adaptation outperformed a baseline. In addition, slight improvement by the style transfer was observed. | https://aclanthology.org/2020.iwslt-1.21 | https://aclanthology.org/2020.iwslt-1.21.pdf | WS 2020 7 | [
"Ryo Fukuda",
"Katsuhito Sudoh",
"Satoshi Nakamura"
] | [
"Domain Adaptation",
"Machine Translation",
"Style Transfer"
] | 1,593,561,600,000 | [] | 124,264 |
124,349 | https://paperswithcode.com/paper/influence-aware-memory-for-deep-reinforcement-1 | 1911.07643 | Influence-aware Memory Architectures for Deep Reinforcement Learning | Due to its perceptual limitations, an agent may have too little information about the state of the environment to act optimally. In such cases, it is important to keep track of the observation history to uncover hidden state. Recent deep reinforcement learning methods use recurrent neural networks (RNN) to memorize past observations. However, these models are expensive to train and have convergence difficulties, especially when dealing with high dimensional input spaces. In this paper, we propose influence-aware memory (IAM), a theoretically inspired memory architecture that tries to alleviate the training difficulties by restricting the input of the recurrent layers to those variables that influence the hidden state information. Moreover, as opposed to standard RNNs, in which every piece of information used for estimating Q values is inevitably fed back into the network for the next prediction, our model allows information to flow without being necessarily stored in the RNN's internal memory. Results indicate that, by letting the recurrent layers focus on a small fraction of the observation variables while processing the rest of the information with a feedforward neural network, we can outperform standard recurrent architectures both in training speed and policy performance. This approach also reduces runtime and obtains better scores than methods that stack multiple observations to remove partial observability. | https://arxiv.org/abs/1911.07643v4 | https://arxiv.org/pdf/1911.07643v4.pdf | null | [
"Miguel Suau",
"Jinke He",
"Elena Congeduti",
"Rolf A. N. Starre",
"Aleksander Czechowski",
"Frans A. Oliehoek"
] | [
"reinforcement-learning"
] | 1,574,035,200,000 | [] | 166,238 |
101,001 | https://paperswithcode.com/paper/deep-unified-multimodal-embeddings-for | 1905.07075 | Deep Unified Multimodal Embeddings for Understanding both Content and Users in Social Media Networks | There has been an explosion of multimodal content generated on social media networks in the last few years, which has necessitated a deeper understanding of social media content and user behavior. We present a novel content-independent content-user-reaction model for social multimedia content analysis. Compared to prior works that generally tackle semantic content understanding and user behavior modeling in isolation, we propose a generalized solution to these problems within a unified framework. We embed users, images and text drawn from open social media in a common multimodal geometric space, using a novel loss function designed to cope with distant and disparate modalities, and thereby enable seamless three-way retrieval. Our model not only outperforms unimodal embedding based methods on cross-modal retrieval tasks but also shows improvements stemming from jointly solving the two tasks on Twitter data. We also show that the user embeddings learned within our joint multimodal embedding model are better at predicting user interests compared to those learned with unimodal content on Instagram data. Our framework thus goes beyond the prior practice of using explicit leader-follower link information to establish affiliations by extracting implicit content-centric affiliations from isolated users. We provide qualitative results to show that the user clusters emerging from learned embeddings have consistent semantics and the ability of our model to discover fine-grained semantics from noisy and unstructured data. Our work reveals that social multimodal content is inherently multimodal and possesses a consistent structure because in social networks meaning is created through interactions between users and content. | https://arxiv.org/abs/1905.07075v3 | https://arxiv.org/pdf/1905.07075v3.pdf | null | [
"Karan Sikka",
"Lucas Van Bramer",
"Ajay Divakaran"
] | [
"Cross-Modal Retrieval"
] | 1,558,051,200,000 | [] | 108,730 |
105,815 | https://paperswithcode.com/paper/few-shot-learning-with-per-sample-rich | 1906.03859 | Few-Shot Learning with Per-Sample Rich Supervision | Learning with few samples is a major challenge for parameter-rich models like deep networks. In contrast, people learn complex new concepts even from very few examples, suggesting that the sample complexity of learning can often be reduced. Many approaches to few-shot learning build on transferring a representation from well-sampled classes, or using meta learning to favor architectures that can learn with few samples. Unfortunately, such approaches often struggle when learning in an online way or with non-stationary data streams. Here we describe a new approach to learn with fewer samples, by using additional information that is provided per sample. Specifically, we show how the sample complexity can be reduced by providing semantic information about the relevance of features per sample, like information about the presence of objects in a scene or confidence of detecting attributes in an image. We provide an improved generalization error bound for this case. We cast the problem of using per-sample feature relevance by using a new ellipsoid-margin loss, and develop an online algorithm that minimizes this loss effectively. Empirical evaluation on two machine vision benchmarks for scene classification and fine-grain bird classification demonstrate the benefits of this approach for few-shot learning. | https://arxiv.org/abs/1906.03859v1 | https://arxiv.org/pdf/1906.03859v1.pdf | null | [
"Roman Visotsky",
"Yuval Atzmon",
"Gal Chechik"
] | [
"Few-Shot Learning",
"Classification",
"Meta-Learning",
"Scene Classification"
] | 1,560,124,800,000 | [] | 81,212 |
9,528 | https://paperswithcode.com/paper/constrained-image-generation-using-binarized | 1802.08795 | Constrained Image Generation Using Binarized Neural Networks with Decision Procedures | We consider the problem of binary image generation with given properties.
This problem arises in a number of practical applications, including generation
of artificial porous medium for an electrode of lithium-ion batteries, for
composed materials, etc. A generated image represents a porous medium and, as
such, it is subject to two sets of constraints: topological constraints on the
structure and process constraints on the physical process over this structure.
To perform image generation we need to define a mapping from a porous medium to
its physical process parameters. For a given geometry of a porous medium, this
mapping can be done by solving a partial differential equation (PDE). However,
embedding a PDE solver into the search procedure is computationally expensive.
We use a binarized neural network to approximate a PDE solver. This allows us
to encode the entire problem as a logical formula. Our main contribution is
that, for the first time, we show that this problem can be tackled using
decision procedures. Our experiments show that our model is able to produce
random constrained images that satisfy both topological and process
constraints. | http://arxiv.org/abs/1802.08795v1 | http://arxiv.org/pdf/1802.08795v1.pdf | null | [
"Svyatoslav Korneev",
"Nina Narodytska",
"Luca Pulina",
"Armando Tacchella",
"Nikolaj Bjorner",
"Mooly Sagiv"
] | [
"Image Generation"
] | 1,519,430,400,000 | [] | 121,913 |
63,916 | https://paperswithcode.com/paper/learning-to-predict-denotational | null | Learning to Predict Denotational Probabilities For Modeling Entailment | We propose a framework that captures the denotational probabilities of words and phrases by embedding them in a vector space, and present a method to induce such an embedding from a dataset of denotational probabilities. We show that our model successfully predicts denotational probabilities for unseen phrases, and that its predictions are useful for textual entailment datasets such as SICK and SNLI. | https://aclanthology.org/E17-1068 | https://aclanthology.org/E17-1068.pdf | EACL 2017 4 | [
"Alice Lai",
"Julia Hockenmaier"
] | [
"Coreference Resolution",
"Natural Language Inference"
] | 1,491,004,800,000 | [] | 74,007 |
201,003 | https://paperswithcode.com/paper/adversarially-guided-actor-critic-1 | 2102.04376 | Adversarially Guided Actor-Critic | Despite definite success in deep reinforcement learning problems, actor-critic algorithms are still confronted with sample inefficiency in complex environments, particularly in tasks where efficient exploration is a bottleneck. These methods consider a policy (the actor) and a value function (the critic) whose respective losses are built using different motivations and approaches. This paper introduces a third protagonist: the adversary. While the adversary mimics the actor by minimizing the KL-divergence between their respective action distributions, the actor, in addition to learning to solve the task, tries to differentiate itself from the adversary predictions. This novel objective stimulates the actor to follow strategies that could not have been correctly predicted from previous trajectories, making its behavior innovative in tasks where the reward is extremely rare. Our experimental analysis shows that the resulting Adversarially Guided Actor-Critic (AGAC) algorithm leads to more exhaustive exploration. Notably, AGAC outperforms current state-of-the-art methods on a set of various hard-exploration and procedurally-generated tasks. | https://arxiv.org/abs/2102.04376v1 | https://arxiv.org/pdf/2102.04376v1.pdf | ICLR 2021 1 | [
"Yannis Flet-Berliac",
"Johan Ferret",
"Olivier Pietquin",
"Philippe Preux",
"Matthieu Geist"
] | [
"Efficient Exploration"
] | 1,612,742,400,000 | [] | 50,348 |
75,241 | https://paperswithcode.com/paper/generative-entity-networks-disentangling | null | Generative Entity Networks: Disentangling Entitites and Attributes in Visual Scenes using Partial Natural Language Descriptions | Generative image models have made significant progress in the last few years, and are now able to generate low-resolution images which sometimes look realistic. However the state-of-the-art models utilize fully entangled latent representations where small changes to a single neuron can effect every output pixel in relatively arbitrary ways, and different neurons have possibly arbitrary relationships with each other. This limits the ability of such models to generalize to new combinations or orientations of objects as well as their ability to connect with more structured representations such as natural language, without explicit strong supervision. In this work explore the synergistic effect of using partial natural language scene descriptions to help disentangle the latent entities visible an image. We present a novel neural network architecture called Generative Entity Networks, which jointly generates both the natural language descriptions and the images from a set of latent entities. Our model is based on the variational autoencoder framework and makes use of visual attention to identify and characterise the visual attributes of each entity. Using the Shapeworld dataset, we show that our representation both enables a better generative model of images, leading to higher quality image samples, as well as creating more semantically useful representations that improve performance over purely dicriminative models on a simple natural language yes/no question answering task. | https://openreview.net/forum?id=BJInMmWC- | https://openreview.net/pdf?id=BJInMmWC- | ICLR 2018 1 | [
"Charlie Nash",
"Sebastian Nowozin",
"Nate Kushman"
] | [
"Question Answering"
] | 1,514,764,800,000 | [
{
"code_snippet_url": "https://github.com/L1aoXingyu/pytorch-beginner/blob/9c86be785c7c318a09cf29112dd1f1a58613239b/08-AutoEncoder/simple_autoencoder.py#L38",
"description": "An **Autoencoder** is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and... | 5,299 |
298,219 | https://paperswithcode.com/paper/where-are-my-neighbors-exploiting-patches | 2206.00481 | Where are my Neighbors? Exploiting Patches Relations in Self-Supervised Vision Transformer | Vision Transformers (ViTs) enabled the use of transformer architecture on vision tasks showing impressive performances when trained on big datasets. However, on relatively small datasets, ViTs are less accurate given their lack of inductive bias. To this end, we propose a simple but still effective self-supervised learning (SSL) strategy to train ViTs, that without any external annotation, can significantly improve the results. Specifically, we define a set of SSL tasks based on relations of image patches that the model has to solve before or jointly during the downstream training. Differently from ViT, our RelViT model optimizes all the output tokens of the transformer encoder that are related to the image patches, thus exploiting more training signal at each training step. We investigated our proposed methods on several image benchmarks finding that RelViT improves the SSL state-of-the-art methods by a large margin, especially on small datasets. | https://arxiv.org/abs/2206.00481v1 | https://arxiv.org/pdf/2206.00481v1.pdf | null | [
"Guglielmo Camporese",
"Elena Izzo",
"Lamberto Ballan"
] | [
"Inductive Bias",
"Self-Supervised Learning"
] | 1,654,041,600,000 | [] | 192,503 |
197,581 | https://paperswithcode.com/paper/fakebuster-a-deepfakes-detection-tool-for | 2101.03321 | FakeBuster: A DeepFakes Detection Tool for Video Conferencing Scenarios | This paper proposes a new DeepFake detector FakeBuster for detecting impostors during video conferencing and manipulated faces on social media. FakeBuster is a standalone deep learning based solution, which enables a user to detect if another person's video is manipulated or spoofed during a video conferencing based meeting. This tool is independent of video conferencing solutions and has been tested with Zoom and Skype applications. It uses a 3D convolutional neural network for predicting video segment-wise fakeness scores. The network is trained on a combination of datasets such as Deeperforensics, DFDC, VoxCeleb, and deepfake videos created using locally captured (for video conferencing scenarios) images. This leads to different environments and perturbations in the dataset, which improves the generalization of the deepfake network. | https://arxiv.org/abs/2101.03321v1 | https://arxiv.org/pdf/2101.03321v1.pdf | null | [
"Vineet Mehta",
"Parul Gupta",
"Ramanathan Subramanian",
"Abhinav Dhall"
] | [
"Face Swapping"
] | 1,610,150,400,000 | [] | 5,388 |
168,778 | https://paperswithcode.com/paper/a-deep-learning-based-interactive-sketching | 2010.04413 | A deep learning based interactive sketching system for fashion images design | In this work, we propose an interactive system to design diverse high-quality garment images from fashion sketches and the texture information. The major challenge behind this system is to generate high-quality and detailed texture according to the user-provided texture information. Prior works mainly use the texture patch representation and try to map a small texture patch to a whole garment image, hence unable to generate high-quality details. In contrast, inspired by intrinsic image decomposition, we decompose this task into texture synthesis and shading enhancement. In particular, we propose a novel bi-colored edge texture representation to synthesize textured garment images and a shading enhancer to render shading based on the grayscale edges. The bi-colored edge representation provides simple but effective texture cues and color constraints, so that the details can be better reconstructed. Moreover, with the rendered shading, the synthesized garment image becomes more vivid. | https://arxiv.org/abs/2010.04413v1 | https://arxiv.org/pdf/2010.04413v1.pdf | null | [
"Yao Li",
"Xianggang Yu",
"Xiaoguang Han",
"Nianjuan Jiang",
"Kui Jia",
"Jiangbo Lu"
] | [
"Intrinsic Image Decomposition",
"Texture Synthesis"
] | 1,602,201,600,000 | [] | 17,119 |
227,557 | https://paperswithcode.com/paper/reinforcement-learning-based-dialogue-guided | 2106.12384 | Reinforcement Learning-based Dialogue Guided Event Extraction to Exploit Argument Relations | Event extraction is a fundamental task for natural language processing. Finding the roles of event arguments like event participants is essential for event extraction. However, doing so for real-life event descriptions is challenging because an argument's role often varies in different contexts. While the relationship and interactions between multiple arguments are useful for settling the argument roles, such information is largely ignored by existing approaches. This paper presents a better approach for event extraction by explicitly utilizing the relationships of event arguments. We achieve this through a carefully designed task-oriented dialogue system. To model the argument relation, we employ reinforcement learning and incremental learning to extract multiple arguments via a multi-turned, iterative process. Our approach leverages knowledge of the already extracted arguments of the same sentence to determine the role of arguments that would be difficult to decide individually. It then uses the newly obtained information to improve the decisions of previously extracted arguments. This two-way feedback process allows us to exploit the argument relations to effectively settle argument roles, leading to better sentence understanding and event extraction. Experimental results show that our approach consistently outperforms seven state-of-the-art event extraction methods for the classification of events and argument role and argument identification. | https://arxiv.org/abs/2106.12384v2 | https://arxiv.org/pdf/2106.12384v2.pdf | null | [
"Qian Li",
"Hao Peng",
"JianXin Li",
"Jia Wu",
"Yuanxing Ning",
"Lihong Wang",
"Philip S. Yu",
"Zheng Wang"
] | [
"Event Extraction",
"Incremental Learning",
"reinforcement-learning"
] | 1,624,406,400,000 | [] | 134,800 |
26,039 | https://paperswithcode.com/paper/adversarial-examples-for-generative-models | 1702.06832 | Adversarial examples for generative models | We explore methods of producing adversarial examples on deep generative
models such as the variational autoencoder (VAE) and the VAE-GAN. Deep learning
architectures are known to be vulnerable to adversarial examples, but previous
work has focused on the application of adversarial examples to classification
tasks. Deep generative models have recently become popular due to their ability
to model input data distributions and generate realistic examples from those
distributions. We present three classes of attacks on the VAE and VAE-GAN
architectures and demonstrate them against networks trained on MNIST, SVHN and
CelebA. Our first attack leverages classification-based adversaries by
attaching a classifier to the trained encoder of the target generative model,
which can then be used to indirectly manipulate the latent representation. Our
second attack directly uses the VAE loss function to generate a target
reconstruction image from the adversarial example. Our third attack moves
beyond relying on classification or the standard loss for the gradient and
directly optimizes against differences in source and target latent
representations. We also motivate why an attacker might be interested in
deploying such techniques against a target generative network. | http://arxiv.org/abs/1702.06832v1 | http://arxiv.org/pdf/1702.06832v1.pdf | null | [
"Jernej Kos",
"Ian Fischer",
"Dawn Song"
] | [
"Classification",
"Classification"
] | 1,487,721,600,000 | [
{
"code_snippet_url": "https://github.com/L1aoXingyu/pytorch-beginner/blob/9c86be785c7c318a09cf29112dd1f1a58613239b/08-AutoEncoder/simple_autoencoder.py#L38",
"description": "An **Autoencoder** is a bottleneck architecture that turns a high-dimensional input into a latent low-dimensional code (encoder), and... | 153,759 |
279,975 | https://paperswithcode.com/paper/cake-a-scalable-commonsense-aware-framework | 2202.13785 | CAKE: A Scalable Commonsense-Aware Framework For Multi-View Knowledge Graph Completion | Knowledge graphs store a large number of factual triples while they are still incomplete, inevitably. The previous knowledge graph completion (KGC) models predict missing links between entities merely relying on fact-view data, ignoring the valuable commonsense knowledge. The previous knowledge graph embedding (KGE) techniques suffer from invalid negative sampling and the uncertainty of fact-view link prediction, limiting KGC's performance. To address the above challenges, we propose a novel and scalable Commonsense-Aware Knowledge Embedding (CAKE) framework to automatically extract commonsense from factual triples with entity concepts. The generated commonsense augments effective self-supervision to facilitate both high-quality negative sampling (NS) and joint commonsense and fact-view link prediction. Experimental results on the KGC task demonstrate that assembling our framework could enhance the performance of the original KGE models, and the proposed commonsense-aware NS module is superior to other NS techniques. Besides, our proposed framework could be easily adaptive to various KGE models and explain the predicted results. | https://arxiv.org/abs/2202.13785v3 | https://arxiv.org/pdf/2202.13785v3.pdf | ACL 2022 5 | [
"Guanglin Niu",
"Bo Li",
"Yongfei Zhang",
"ShiLiang Pu"
] | [
"Graph Embedding",
"Knowledge Graph Completion",
"Knowledge Graph Embedding",
"Knowledge Graphs",
"Link Prediction"
] | 1,645,747,200,000 | [] | 53,744 |
184,651 | https://paperswithcode.com/paper/mufold-betaturn-a-deep-dense-inception | 1808.04322 | MUFold-BetaTurn: A Deep Dense Inception Network for Protein Beta-Turn Prediction | Beta-turn prediction is useful in protein function studies and experimental
design. Although recent approaches using machine-learning techniques such as
SVM, neural networks, and K-NN have achieved good results for beta-turn
pre-diction, there is still significant room for improvement. As previous
predictors utilized features in a sliding window of 4-20 residues to capture
interactions among sequentially neighboring residues, such feature engineering
may result in incomplete or biased features, and neglect interactions among
long-range residues. Deep neural networks provide a new opportunity to address
these issues. Here, we proposed a deep dense inception network (DeepDIN) for
beta-turn prediction, which takes advantages of the state-of-the-art deep
neural network design of the DenseNet and the inception network. A test on a
recent BT6376 benchmark shows that the DeepDIN outperformed the previous best
BetaTPred3 significantly in both the overall prediction accuracy and the
nine-type beta-turn classification. A tool, called MUFold-BetaTurn, was
developed, which is the first beta-turn prediction tool utilizing deep neural
networks. The tool can be downloaded at
http://dslsrv8.cs.missouri.edu/~cf797/MUFoldBetaTurn/download.html. | http://arxiv.org/abs/1808.04322v1 | http://arxiv.org/pdf/1808.04322v1.pdf | null | [] | [
"Experimental Design",
"Feature Engineering"
] | 1,534,118,400,000 | [] | 97,061 |
137,241 | https://paperswithcode.com/paper/pool-based-unsupervised-active-learning-for | 2003.07658 | Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM) | Active learning (AL) selects the most beneficial unlabeled samples to label, and hence a better machine learning model can be trained from the same number of labeled samples. Most existing active learning for regression (ALR) approaches are supervised, which means the sampling process must use some label information, or an existing regression model. This paper considers completely unsupervised ALR, i.e., how to select the samples to label without knowing any true label information. We propose a novel unsupervised ALR approach, iterative representativeness-diversity maximization (iRDM), to optimally balance the representativeness and the diversity of the selected samples. Experiments on 12 datasets from various domains demonstrated its effectiveness. Our iRDM can be applied to both linear regression and kernel regression, and it even significantly outperforms supervised ALR when the number of labeled samples is small. | https://arxiv.org/abs/2003.07658v2 | https://arxiv.org/pdf/2003.07658v2.pdf | null | [
"Ziang Liu",
"Xue Jiang",
"Hanbin Luo",
"Weili Fang",
"Jiajing Liu",
"Dongrui Wu"
] | [
"Active Learning"
] | 1,584,403,200,000 | [
{
"code_snippet_url": null,
"description": "**Linear Regression** is a method for modelling a relationship between a dependent variable and independent variables. These models can be fit with numerous approaches. The most common is *least squares*, where we minimize the mean square error between the predict... | 120,211 |
293,867 | https://paperswithcode.com/paper/cross-modal-cloze-task-a-new-task-to-brain-to | null | Cross-Modal Cloze Task: A New Task to Brain-to-Word Decoding | Decoding language from non-invasive brain activity has attracted increasing attention from both researchers in neuroscience and natural language processing. Due to the noisy nature of brain recordings, existing work has simplified brain-to-word decoding as a binary classification task which is to discriminate a brain signal between its corresponding word and a wrong one. This pairwise classification task, however, cannot promote the development of practical neural decoders for two reasons. First, it has to enumerate all pairwise combinations in the test set, so it is inefficient to predict a word in a large vocabulary. Second, a perfect pairwise decoder cannot guarantee the performance on direct classification. To overcome these and go a step further to a realistic neural decoder, we propose a novel Cross-Modal Cloze (CMC) task which is to predict the target word encoded in the neural image with a context as prompt. Furthermore, to address this task, we propose a general approach that leverages the pre-trained language model to predict the target word. To validate our method, we perform experiments on more than 20 participants from two brain imaging datasets. Our method achieves 28.91% top-1 accuracy and 54.19% top-5 accuracy on average across all participants, significantly outperforming several baselines. This result indicates that our model can serve as a state-of-the-art baseline for the CMC task. More importantly, it demonstrates that it is feasible to decode a certain word within a large vocabulary from its neural brain activity. | https://aclanthology.org/2022.findings-acl.54 | https://aclanthology.org/2022.findings-acl.54.pdf | Findings (ACL) 2022 5 | [
"Shuxian Zou",
"Shaonan Wang",
"Jiajun Zhang",
"Chengqing Zong"
] | [
"Language Modelling"
] | 1,651,363,200,000 | [] | 154,832 |
227,847 | https://paperswithcode.com/paper/bayesian-inference-in-high-dimensional-time-1 | 2106.13379 | Bayesian Inference in High-Dimensional Time-Serieswith the Orthogonal Stochastic Linear Mixing Model | Many modern time-series datasets contain large numbers of output response variables sampled for prolonged periods of time. For example, in neuroscience, the activities of 100s-1000's of neurons are recorded during behaviors and in response to sensory stimuli. Multi-output Gaussian process models leverage the nonparametric nature of Gaussian processes to capture structure across multiple outputs. However, this class of models typically assumes that the correlations between the output response variables are invariant in the input space. Stochastic linear mixing models (SLMM) assume the mixture coefficients depend on input, making them more flexible and effective to capture complex output dependence. However, currently, the inference for SLMMs is intractable for large datasets, making them inapplicable to several modern time-series problems. In this paper, we propose a new regression framework, the orthogonal stochastic linear mixing model (OSLMM) that introduces an orthogonal constraint amongst the mixing coefficients. This constraint reduces the computational burden of inference while retaining the capability to handle complex output dependence. We provide Markov chain Monte Carlo inference procedures for both SLMM and OSLMM and demonstrate superior model scalability and reduced prediction error of OSLMM compared with state-of-the-art methods on several real-world applications. In neurophysiology recordings, we use the inferred latent functions for compact visualization of population responses to auditory stimuli, and demonstrate superior results compared to a competing method (GPFA). Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale time-series datasets. | https://arxiv.org/abs/2106.13379v2 | https://arxiv.org/pdf/2106.13379v2.pdf | null | [
"Rui Meng",
"Kristofer Bouchard"
] | [
"Bayesian Inference",
"Gaussian Processes",
"Time Series"
] | 1,624,579,200,000 | [
{
"code_snippet_url": null,
"description": "**Gaussian Processes** are non-parametric models for approximating functions. They rely upon a measure of similarity between points (the kernel function) to predict the value for an unseen point from training data. The models are fully probabilistic so uncertainty... | 102,352 |
236,184 | https://paperswithcode.com/paper/modulating-language-models-with-emotions | 2108.07886 | Modulating Language Models with Emotions | Generating context-aware language that embodies diverse emotions is an important step towards building empathetic NLP systems. In this paper, we propose a formulation of modulated layer normalization -- a technique inspired by computer vision -- that allows us to use large-scale language models for emotional response generation. In automatic and human evaluation on the MojiTalk dataset, our proposed modulated layer normalization method outperforms prior baseline methods while maintaining diversity, fluency, and coherence. Our method also obtains competitive performance even when using only 10% of the available training data. | https://arxiv.org/abs/2108.07886v1 | https://arxiv.org/pdf/2108.07886v1.pdf | Findings (ACL) 2021 8 | [
"Ruibo Liu",
"Jason Wei",
"Chenyan Jia",
"Soroush Vosoughi"
] | [
"Response Generation"
] | 1,629,158,400,000 | [
{
"code_snippet_url": "https://github.com/CyberZHG/torch-layer-normalization/blob/89f405b60f53f85da6f03fe685c190ef394ce50c/torch_layer_normalization/layer_normalization.py#L8",
"description": "Unlike [batch normalization](https://paperswithcode.com/method/batch-normalization), **Layer Normalization** direct... | 97,900 |
290,977 | https://paperswithcode.com/paper/defending-against-person-hiding-adversarial | 2204.13004 | Defending Against Person Hiding Adversarial Patch Attack with a Universal White Frame | Object detection has attracted great attention in the computer vision area and has emerged as an indispensable component in many vision systems. In the era of deep learning, many high-performance object detection networks have been proposed. Although these detection networks show high performance, they are vulnerable to adversarial patch attacks. Changing the pixels in a restricted region can easily fool the detection network in the physical world. In particular, person-hiding attacks are emerging as a serious problem in many safety-critical applications such as autonomous driving and surveillance systems. Although it is necessary to defend against an adversarial patch attack, very few efforts have been dedicated to defending against person-hiding attacks. To tackle the problem, in this paper, we propose a novel defense strategy that mitigates a person-hiding attack by optimizing defense patterns, while previous methods optimize the model. In the proposed method, a frame-shaped pattern called a 'universal white frame' (UWF) is optimized and placed on the outside of the image. To defend against adversarial patch attacks, UWF should have three properties (i) suppressing the effect of the adversarial patch, (ii) maintaining its original prediction, and (iii) applicable regardless of images. To satisfy the aforementioned properties, we propose a novel pattern optimization algorithm that can defend against the adversarial patch. Through comprehensive experiments, we demonstrate that the proposed method effectively defends against the adversarial patch attack. | https://arxiv.org/abs/2204.13004v1 | https://arxiv.org/pdf/2204.13004v1.pdf | null | [
"Youngjoon Yu",
"Hong Joo Lee",
"Hakmin Lee",
"Yong Man Ro"
] | [
"Autonomous Driving",
"Object Detection",
"Object Detection"
] | 1,651,017,600,000 | [] | 191,602 |
290,047 | https://paperswithcode.com/paper/towards-fewer-labels-support-pair-active | 2204.10008 | Towards Fewer Labels: Support Pair Active Learning for Person Re-identification | Supervised-learning based person re-identification (re-id) require a large amount of manual labeled data, which is not applicable in practical re-id deployment. In this work, we propose a Support Pair Active Learning (SPAL) framework to lower the manual labeling cost for large-scale person reidentification. The support pairs can provide the most informative relationships and support the discriminative feature learning. Specifically, we firstly design a dual uncertainty selection strategy to iteratively discover support pairs and require human annotations. Afterwards, we introduce a constrained clustering algorithm to propagate the relationships of labeled support pairs to other unlabeled samples. Moreover, a hybrid learning strategy consisting of an unsupervised contrastive loss and a supervised support pair loss is proposed to learn the discriminative re-id feature representation. The proposed overall framework can effectively lower the labeling cost by mining and leveraging the critical support pairs. Extensive experiments demonstrate the superiority of the proposed method over state-of-the-art active learning methods on large-scale person re-id benchmarks. | https://arxiv.org/abs/2204.10008v1 | https://arxiv.org/pdf/2204.10008v1.pdf | null | [
"Dapeng Jin",
"Minxian Li"
] | [
"Active Learning",
"Person Re-Identification"
] | 1,650,499,200,000 | [] | 22,530 |
822 | https://paperswithcode.com/paper/addition-of-code-mixed-features-to-enhance | 1806.03821 | Addition of Code Mixed Features to Enhance the Sentiment Prediction of Song Lyrics | Sentiment analysis, also called opinion mining, is the field of study that
analyzes people's opinions,sentiments, attitudes and emotions. Songs are
important to sentiment analysis since the songs and mood are mutually dependent
on each other. Based on the selected song it becomes easy to find the mood of
the listener, in future it can be used for recommendation. The song lyric is a
rich source of datasets containing words that are helpful in analysis and
classification of sentiments generated from it. Now a days we observe a lot of
inter-sentential and intra-sentential code-mixing in songs which has a varying
impact on audience. To study this impact we created a Telugu songs dataset
which contained both Telugu-English code-mixed and pure Telugu songs. In this
paper, we classify the songs based on its arousal as exciting or non-exciting.
We develop a language identification tool and introduce code-mixing features
obtained from it as additional features. Our system with these additional
features attains 4-5% accuracy greater than traditional approaches on our
dataset. | http://arxiv.org/abs/1806.03821v1 | http://arxiv.org/pdf/1806.03821v1.pdf | null | [
"Gangula Rama Rohit Reddy",
"Radhika Mamidi"
] | [
"Language Identification",
"Opinion Mining",
"Sentiment Analysis"
] | 1,528,675,200,000 | [] | 174,454 |
6,803 | https://paperswithcode.com/paper/multi-lingual-neural-title-generation-for-e | 1804.01041 | Multi-lingual neural title generation for e-Commerce browse pages | To provide better access of the inventory to buyers and better search engine
optimization, e-Commerce websites are automatically generating millions of
easily searchable browse pages. A browse page consists of a set of slot
name/value pairs within a given category, grouping multiple items which share
some characteristics. These browse pages require a title describing the content
of the page. Since the number of browse pages are huge, manual creation of
these titles is infeasible. Previous statistical and neural approaches depend
heavily on the availability of large amounts of data in a language. In this
research, we apply sequence-to-sequence models to generate titles for high- &
low-resourced languages by leveraging transfer learning. We train these models
on multi-lingual data, thereby creating one joint model which can generate
titles in various different languages. Performance of the title generation
system is evaluated on three different languages; English, German, and French,
with a particular focus on low-resourced French language. | http://arxiv.org/abs/1804.01041v1 | http://arxiv.org/pdf/1804.01041v1.pdf | NAACL 2018 6 | [
"Prashant Mathur",
"Nicola Ueffing",
"Gregor Leusch"
] | [
"Transfer Learning"
] | 1,522,713,600,000 | [] | 185,413 |
193,153 | https://paperswithcode.com/paper/understanding-interpretability-by-generalized | 2012.03089 | Understanding Interpretability by generalized distillation in Supervised Classification | The ability to interpret decisions taken by Machine Learning (ML) models is fundamental to encourage trust and reliability in different practical applications. Recent interpretation strategies focus on human understanding of the underlying decision mechanisms of the complex ML models. However, these strategies are restricted by the subjective biases of humans. To dissociate from such human biases, we propose an interpretation-by-distillation formulation that is defined relative to other ML models. We generalize the distillation technique for quantifying interpretability, using an information-theoretic perspective, removing the role of ground-truth from the definition of interpretability. Our work defines the entropy of supervised classification models, providing bounds on the entropy of Piece-Wise Linear Neural Networks (PWLNs), along with the first theoretical bounds on the interpretability of PWLNs. We evaluate our proposed framework on the MNIST, Fashion-MNIST and Stanford40 datasets and demonstrate the applicability of the proposed theoretical framework in different supervised classification scenarios. | https://arxiv.org/abs/2012.03089v1 | https://arxiv.org/pdf/2012.03089v1.pdf | null | [
"Adit Agarwal",
"Dr. K. K. Shukla",
"Arjan Kuijper",
"Anirban Mukhopadhyay"
] | [
"Classification",
"Classification"
] | 1,607,126,400,000 | [
{
"code_snippet_url": null,
"description": "Please enter a description about the method here",
"full_name": "Interpretability",
"introduced_year": 2000,
"main_collection": {
"area": "Computer Vision",
"description": "**Image Models** are methods that build representations of images f... | 60,649 |
313,207 | https://paperswithcode.com/paper/improving-multilayer-perceptron-mlp-based | 2208.09711 | Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset | Machine learning algorithms have been widely used in intrusion detection systems, including Multi-layer Perceptron (MLP). In this study, we proposed a two-stage model that combines the Birch clustering algorithm and MLP classifier to improve the performance of network anomaly multi-classification. In our proposed method, we first apply Birch or Kmeans as an unsupervised clustering algorithm to the CICIDS-2017 dataset to pre-group the data. The generated pseudo-label is then added as an additional feature to the training of the MLP-based classifier. The experimental results show that using Birch and K-Means clustering for data pre-grouping can improve intrusion detection system performance. Our method can achieve 99.73% accuracy in multi-classification using Birch clustering, which is better than similar researches using a stand-alone MLP model. | https://arxiv.org/abs/2208.09711v1 | https://arxiv.org/pdf/2208.09711v1.pdf | null | [
"Yuhua Yin",
"Julian Jang-Jaccard",
"Fariza Sabrina",
"Jin Kwak"
] | [
"Anomaly Detection",
"Intrusion Detection",
"pseudo label"
] | 1,660,953,600,000 | [
{
"code_snippet_url": "https://cryptoabout.info",
"description": "**k-Means Clustering** is a clustering algorithm that divides a training set into $k$ different clusters of examples that are near each other. It works by initializing $k$ different centroids {$\\mu\\left(1\\right),\\ldots,\\mu\\left(k\\right... | 92,023 |
52,195 | https://paperswithcode.com/paper/twitter-sentiment-analysis-via-bi-sense-emoji | 1807.07961 | Twitter Sentiment Analysis via Bi-sense Emoji Embedding and Attention-based LSTM | Sentiment analysis on large-scale social media data is important to bridge
the gaps between social media contents and real world activities including
political election prediction, individual and public emotional status
monitoring and analysis, and so on. Although textual sentiment analysis has
been well studied based on platforms such as Twitter and Instagram, analysis of
the role of extensive emoji uses in sentiment analysis remains light. In this
paper, we propose a novel scheme for Twitter sentiment analysis with extra
attention on emojis. We first learn bi-sense emoji embeddings under positive
and negative sentimental tweets individually, and then train a sentiment
classifier by attending on these bi-sense emoji embeddings with an
attention-based long short-term memory network (LSTM). Our experiments show
that the bi-sense embedding is effective for extracting sentiment-aware
embeddings of emojis and outperforms the state-of-the-art models. We also
visualize the attentions to show that the bi-sense emoji embedding provides
better guidance on the attention mechanism to obtain a more robust
understanding of the semantics and sentiments. | http://arxiv.org/abs/1807.07961v2 | http://arxiv.org/pdf/1807.07961v2.pdf | null | [
"Yuxiao Chen",
"Jianbo Yuan",
"Quanzeng You",
"Jiebo Luo"
] | [
"Sentiment Analysis",
"Twitter Sentiment Analysis"
] | 1,532,044,800,000 | [
{
"code_snippet_url": "https://github.com/aykutaaykut/Memory-Networks",
"description": "A **Memory Network** provides a memory component that can be read from and written to with the inference capabilities of a neural network model. The motivation is that many neural networks lack a long-term memory compone... | 87,823 |
164,737 | https://paperswithcode.com/paper/an-incentive-mechanism-for-federated-learning | 2009.10269 | An Incentive Mechanism for Federated Learning in Wireless Cellular network: An Auction Approach | Federated Learning (FL) is a distributed learning framework that can deal with the distributed issue in machine learning and still guarantee high learning performance. However, it is impractical that all users will sacrifice their resources to join the FL algorithm. This motivates us to study the incentive mechanism design for FL. In this paper, we consider a FL system that involves one base station (BS) and multiple mobile users. The mobile users use their own data to train the local machine learning model, and then send the trained models to the BS, which generates the initial model, collects local models and constructs the global model. Then, we formulate the incentive mechanism between the BS and mobile users as an auction game where the BS is an auctioneer and the mobile users are the sellers. In the proposed game, each mobile user submits its bids according to the minimal energy cost that the mobile users experiences in participating in FL. To decide winners in the auction and maximize social welfare, we propose the primal-dual greedy auction mechanism. The proposed mechanism can guarantee three economic properties, namely, truthfulness, individual rationality and efficiency. Finally, numerical results are shown to demonstrate the performance effectiveness of our proposed mechanism. | https://arxiv.org/abs/2009.10269v1 | https://arxiv.org/pdf/2009.10269v1.pdf | null | [
"Tra Huong Thi Le",
"Nguyen H. Tran",
"Yan Kyaw Tun",
"Minh N. H. Nguyen",
"Shashi Raj Pandey",
"Zhu Han",
"Choong Seon Hong"
] | [
"Federated Learning"
] | 1,600,732,800,000 | [] | 25,683 |
314,754 | https://paperswithcode.com/paper/spoofing-aware-attention-based-asv-back-end | 2209.00423 | Spoofing-Aware Attention based ASV Back-end with Multiple Enrollment Utterances and a Sampling Strategy for the SASV Challenge 2022 | Current state-of-the-art automatic speaker verification (ASV) systems are vulnerable to presentation attacks, and several countermeasures (CMs), which distinguish bona fide trials from spoofing ones, have been explored to protect ASV. However, ASV systems and CMs are generally developed and optimized independently without considering their inter-relationship. In this paper, we propose a new spoofing-aware ASV back-end module that efficiently computes a combined ASV score based on speaker similarity and CM score. In addition to the learnable fusion function of the two scores, the proposed back-end module has two types of attention components, scaled-dot and feed-forward self-attention, so that intra-relationship information of multiple enrollment utterances can also be learned at the same time. Moreover, a new effective trials-sampling strategy is designed for simulating new spoofing-aware verification scenarios introduced in the Spoof-Aware Speaker Verification (SASV) challenge 2022. | https://arxiv.org/abs/2209.00423v1 | https://arxiv.org/pdf/2209.00423v1.pdf | null | [
"Chang Zeng",
"Lin Zhang",
"Meng Liu",
"Junichi Yamagishi"
] | [
"Speaker Verification"
] | 1,661,990,400,000 | [] | 186,256 |
256,745 | https://paperswithcode.com/paper/parbleu-augmenting-metrics-with-automatic | null | ParBLEU: Augmenting Metrics with Automatic Paraphrases for the WMT’20 Metrics Shared Task | We describe parBLEU, parCHRF++, and parESIM, which augment baseline metrics with automatically generated paraphrases produced by PRISM (Thompson and Post, 2020a), a multilingual neural machine translation system. We build on recent work studying how to improve BLEU by using diverse automatically paraphrased references (Bawden et al., 2020), extending experiments to the multilingual setting for the WMT2020 metrics shared task and for three base metrics. We compare their capacity to exploit up to 100 additional synthetic references. We find that gains are possible when using additional, automatically paraphrased references, although they are not systematic. However, segment-level correlations, particularly into English, are improved for all three metrics and even with higher numbers of paraphrased references. | https://aclanthology.org/2020.wmt-1.98 | https://aclanthology.org/2020.wmt-1.98.pdf | WMT (EMNLP) 2020 11 | [
"Rachel Bawden",
"Biao Zhang",
"Andre Tättar",
"Matt Post"
] | [
"Machine Translation"
] | 1,604,188,800,000 | [] | 32,834 |
207,192 | https://paperswithcode.com/paper/learning-to-simulate-on-sparse-trajectory | 2103.11845 | Learning to Simulate on Sparse Trajectory Data | Simulation of the real-world traffic can be used to help validate the transportation policies. A good simulator means the simulated traffic is similar to real-world traffic, which often requires dense traffic trajectories (i.e., with a high sampling rate) to cover dynamic situations in the real world. However, in most cases, the real-world trajectories are sparse, which makes simulation challenging. In this paper, we present a novel framework ImInGAIL to address the problem of learning to simulate the driving behavior from sparse real-world data. The proposed architecture incorporates data interpolation with the behavior learning process of imitation learning. To the best of our knowledge, we are the first to tackle the data sparsity issue for behavior learning problems. We investigate our framework on both synthetic and real-world trajectory datasets of driving vehicles, showing that our method outperforms various baselines and state-of-the-art methods. | https://arxiv.org/abs/2103.11845v1 | https://arxiv.org/pdf/2103.11845v1.pdf | null | [
"Hua Wei",
"Chacha Chen",
"Chang Liu",
"Guanjie Zheng",
"Zhenhui Li"
] | [
"Imitation Learning"
] | 1,616,371,200,000 | [] | 148,197 |
13,588 | https://paperswithcode.com/paper/a-variational-approach-to-shape-from-shading | 1709.10354 | A Variational Approach to Shape-from-shading Under Natural Illumination | A numerical solution to shape-from-shading under natural illumination is
presented. It builds upon an augmented Lagrangian approach for solving a
generic PDE-based shape-from-shading model which handles directional or
spherical harmonic lighting, orthographic or perspective projection, and
greylevel or multi-channel images. Real-world applications to shading-aware
depth map denoising, refinement and completion are presented. | http://arxiv.org/abs/1709.10354v2 | http://arxiv.org/pdf/1709.10354v2.pdf | null | [
"Yvain Quéau",
"Jean Mélou",
"Fabien Castan",
"Daniel Cremers",
"Jean-Denis Durou"
] | [
"Denoising"
] | 1,506,643,200,000 | [] | 131,612 |
212,741 | https://paperswithcode.com/paper/unsupervised-learning-of-explainable-parse | 2104.04998 | Unsupervised Learning of Explainable Parse Trees for Improved Generalisation | Recursive neural networks (RvNN) have been shown useful for learning sentence representations and helped achieve competitive performance on several natural language inference tasks. However, recent RvNN-based models fail to learn simple grammar and meaningful semantics in their intermediate tree representation. In this work, we propose an attention mechanism over Tree-LSTMs to learn more meaningful and explainable parse tree structures. We also demonstrate the superior performance of our proposed model on natural language inference, semantic relatedness, and sentiment analysis tasks and compare them with other state-of-the-art RvNN based methods. Further, we present a detailed qualitative and quantitative analysis of the learned parse trees and show that the discovered linguistic structures are more explainable, semantically meaningful, and grammatically correct than recent approaches. The source code of the paper is available at https://github.com/atul04/Explainable-Latent-Structures-Using-Attention. | https://arxiv.org/abs/2104.04998v1 | https://arxiv.org/pdf/2104.04998v1.pdf | null | [
"Atul Sahay",
"Ayush Maheshwari",
"Ritesh Kumar",
"Ganesh Ramakrishnan",
"Manjesh Kumar Hanawal",
"Kavi Arya"
] | [
"Natural Language Inference",
"Sentiment Analysis"
] | 1,618,099,200,000 | [] | 137,812 |
277,335 | https://paperswithcode.com/paper/towards-weakly-supervised-text-spotting-using | 2202.05508 | Towards Weakly-Supervised Text Spotting using a Multi-Task Transformer | Text spotting end-to-end methods have recently gained attention in the literature due to the benefits of jointly optimizing the text detection and recognition components. Existing methods usually have a distinct separation between the detection and recognition branches, requiring exact annotations for the two tasks. We introduce TextTranSpotter (TTS), a transformer-based approach for text spotting and the first text spotting framework which may be trained with both fully- and weakly-supervised settings. By learning a single latent representation per word detection, and using a novel loss function based on the Hungarian loss, our method alleviates the need for expensive localization annotations. Trained with only text transcription annotations on real data, our weakly-supervised method achieves competitive performance with previous state-of-the-art fully-supervised methods. When trained in a fully-supervised manner, TextTranSpotter shows state-of-the-art results on multiple benchmarks. | https://arxiv.org/abs/2202.05508v2 | https://arxiv.org/pdf/2202.05508v2.pdf | CVPR 2022 1 | [
"Yair Kittenplon",
"Inbal Lavi",
"Sharon Fogel",
"Yarin Bar",
"R. Manmatha",
"Pietro Perona"
] | [
"Text Spotting"
] | 1,644,537,600,000 | [] | 6,532 |
168,919 | https://paperswithcode.com/paper/a-novel-strategy-for-covid-19-classification | 2010.05690 | COVID-19 Classification Using Staked Ensembles: A Comprehensive Analysis | The issue of COVID-19, increasing with a massive mortality rate. This led to the WHO declaring it as a pandemic. In this situation, it is crucial to perform efficient and fast diagnosis. The reverse transcript polymerase chain reaction (RTPCR) test is conducted to detect the presence of SARS-CoV-2. This test is time-consuming and instead chest CT (or Chest X-ray) can be used for a fast and accurate diagnosis. Automated diagnosis is considered to be important as it reduces human effort and provides accurate and low-cost tests. The contributions of our research are three-fold. First, it is aimed to analyse the behaviour and performance of variant vision models ranging from Inception to NAS networks with the appropriate fine-tuning procedure. Second, the behaviour of these models is visually analysed by plotting CAMs for individual networks and determining classification performance with AUCROC curves. Thirdly, stacked ensembles techniques are imparted to provide higher generalisation on combining the fine-tuned models, in which six ensemble neural networks are designed by combining the existing fine-tuned networks. Implying these stacked ensembles provides a great generalization to the models. The ensemble model designed by combining all the fine-tuned networks obtained a state-of-the-art accuracy score of 99.17%. The precision and recall for the COVID-19 class are 99.99% and 89.79% respectively, which resembles the robustness of the stacked ensembles. | https://arxiv.org/abs/2010.05690v3 | https://arxiv.org/pdf/2010.05690v3.pdf | null | [
"Lalith Bharadwaj B",
"Rohit Boddeda",
"Sai Vardhan K",
"Madhu G"
] | [
"Classification"
] | 1,602,028,800,000 | [] | 2,990 |
264,422 | https://paperswithcode.com/paper/multilingual-pre-training-with-language-and | null | Multilingual pre-training with Language and Task Adaptation for Multilingual Text Style Transfer | We exploit the pre-trained seq2seq model mBART for multilingual text style transfer. Using machine translated data as well as gold aligned English sentences yields state-of-the-art results in the three target languages we consider. Besides, in view of the general scarcity of parallel data, we propose a modular approach for multilingual formality transfer, which consists of two training strategies that target adaptation to both language and task. Our approach achieves competitive performance without monolingual task-specific parallel data and can be applied to other style transfer tasks as well as to other languages. | https://openreview.net/forum?id=rWPLdCIiY6g | https://openreview.net/pdf?id=rWPLdCIiY6g | ACL ARR November 2021 11 | [
"Anonymous"
] | [
"Style Transfer",
"Text Style Transfer"
] | 1,637,020,800,000 | [
{
"code_snippet_url": "https://github.com/pytorch/pytorch/blob/96aaa311c0251d24decb9dc5da4957b7c590af6f/torch/nn/modules/activation.py#L329",
"description": "**Tanh Activation** is an activation function used for neural networks:\r\n\r\n$$f\\left(x\\right) = \\frac{e^{x} - e^{-x}}{e^{x} + e^{-x}}$$\r\n\r\nH... | 2,148 |
215,525 | https://paperswithcode.com/paper/discovering-an-aid-policy-to-minimize-student | 2104.10258 | Discovering an Aid Policy to Minimize Student Evasion Using Offline Reinforcement Learning | High dropout rates in tertiary education expose a lack of efficiency that causes frustration of expectations and financial waste. Predicting students at risk is not enough to avoid student dropout. Usually, an appropriate aid action must be discovered and applied in the proper time for each student. To tackle this sequential decision-making problem, we propose a decision support method to the selection of aid actions for students using offline reinforcement learning to support decision-makers effectively avoid student dropout. Additionally, a discretization of student's state space applying two different clustering methods is evaluated. Our experiments using logged data of real students shows, through off-policy evaluation, that the method should achieve roughly 1.0 to 1.5 times as much cumulative reward as the logged policy. So, it is feasible to help decision-makers apply appropriate aid actions and, possibly, reduce student dropout. | https://arxiv.org/abs/2104.10258v1 | https://arxiv.org/pdf/2104.10258v1.pdf | null | [
"Leandro M. de Lima",
"Renato A. Krohling"
] | [
"reinforcement-learning"
] | 1,618,876,800,000 | [
{
"code_snippet_url": "https://github.com/google/jax/blob/7f3078b70d0ed9bea6228efa420879c56f72ef69/jax/experimental/stax.py#L271-L275",
"description": "**Dropout** is a regularization technique for neural networks that drops a unit (along with connections) at training time with a specified probability $p$ (... | 77,804 |
8,616 | https://paperswithcode.com/paper/learning-approximate-inference-networks-for | 1803.03376 | Learning Approximate Inference Networks for Structured Prediction | Structured prediction energy networks (SPENs; Belanger & McCallum 2016) use
neural network architectures to define energy functions that can capture
arbitrary dependencies among parts of structured outputs. Prior work used
gradient descent for inference, relaxing the structured output to a set of
continuous variables and then optimizing the energy with respect to them. We
replace this use of gradient descent with a neural network trained to
approximate structured argmax inference. This "inference network" outputs
continuous values that we treat as the output structure. We develop
large-margin training criteria for joint training of the structured energy
function and inference network. On multi-label classification we report
speed-ups of 10-60x compared to (Belanger et al, 2017) while also improving
accuracy. For sequence labeling with simple structured energies, our approach
performs comparably to exact inference while being much faster at test time. We
then demonstrate improved accuracy by augmenting the energy with a "label
language model" that scores entire output label sequences, showing it can
improve handling of long-distance dependencies in part-of-speech tagging.
Finally, we show how inference networks can replace dynamic programming for
test-time inference in conditional random fields, suggestive for their general
use for fast inference in structured settings. | http://arxiv.org/abs/1803.03376v1 | http://arxiv.org/pdf/1803.03376v1.pdf | ICLR 2018 1 | [
"Lifu Tu",
"Kevin Gimpel"
] | [
"Language Modelling",
"Multi-Label Classification",
"Part-Of-Speech Tagging",
"Structured Prediction"
] | 1,520,553,600,000 | [] | 56,649 |
221,481 | https://paperswithcode.com/paper/stytr-2-unbiased-image-style-transfer-with | 2105.14576 | StyTr$^2$: Image Style Transfer with Transformers | The goal of image style transfer is to render an image with artistic features guided by a style reference while maintaining the original content. Owing to the locality in convolutional neural networks (CNNs), extracting and maintaining the global information of input images is difficult. Therefore, traditional neural style transfer methods face biased content representation. To address this critical issue, we take long-range dependencies of input images into account for image style transfer by proposing a transformer-based approach called StyTr$^2$. In contrast with visual transformers for other vision tasks, StyTr$^2$ contains two different transformer encoders to generate domain-specific sequences for content and style, respectively. Following the encoders, a multi-layer transformer decoder is adopted to stylize the content sequence according to the style sequence. We also analyze the deficiency of existing positional encoding methods and propose the content-aware positional encoding (CAPE), which is scale-invariant and more suitable for image style transfer tasks. Qualitative and quantitative experiments demonstrate the effectiveness of the proposed StyTr$^2$ compared with state-of-the-art CNN-based and flow-based approaches. Code and models are available at https://github.com/diyiiyiii/StyTR-2. | https://arxiv.org/abs/2105.14576v3 | https://arxiv.org/pdf/2105.14576v3.pdf | null | [
"Yingying Deng",
"Fan Tang",
"WeiMing Dong",
"Chongyang Ma",
"Xingjia Pan",
"Lei Wang",
"Changsheng Xu"
] | [
"Style Transfer"
] | 1,622,332,800,000 | [] | 130,489 |
206,830 | https://paperswithcode.com/paper/consistency-based-active-learning-for-object | 2103.10374 | Consistency-based Active Learning for Object Detection | Active learning aims to improve the performance of task model by selecting the most informative samples with a limited budget. Unlike most recent works that focused on applying active learning for image classification, we propose an effective Consistency-based Active Learning method for object Detection (CALD), which fully explores the consistency between original and augmented data. CALD has three appealing benefits. (i) CALD is systematically designed by investigating the weaknesses of existing active learning methods, which do not take the unique challenges of object detection into account. (ii) CALD unifies box regression and classification with a single metric, which is not concerned by active learning methods for classification. CALD also focuses on the most informative local region rather than the whole image, which is beneficial for object detection. (iii) CALD not only gauges individual information for sample selection, but also leverages mutual information to encourage a balanced data distribution. Extensive experiments show that CALD significantly outperforms existing state-of-the-art task-agnostic and detection-specific active learning methods on general object detection datasets. Based on the Faster R-CNN detector, CALD consistently surpasses the baseline method (random selection) by 2.9/2.8/0.8 mAP on average on PASCAL VOC 2007, PASCAL VOC 2012, and MS COCO. Code is available at \url{https://github.com/we1pingyu/CALD} | https://arxiv.org/abs/2103.10374v3 | https://arxiv.org/pdf/2103.10374v3.pdf | null | [
"Weiping Yu",
"Sijie Zhu",
"Taojiannan Yang",
"Chen Chen"
] | [
"Active Learning",
"Classification",
"Classification",
"Image Classification",
"Object Detection",
"Object Detection"
] | 1,616,025,600,000 | [
{
"code_snippet_url": "https://github.com/pytorch/vision/blob/5e9ebe8dadc0ea2841a46cfcd82a93b4ce0d4519/torchvision/ops/roi_pool.py#L10",
"description": "**Region of Interest Pooling**, or **RoIPool**, is an operation for extracting a small feature map (e.g., $7×7$) from each RoI in detection and segmentatio... | 62,718 |
52,784 | https://paperswithcode.com/paper/news-session-based-recommendations-using-deep | 1808.00076 | News Session-Based Recommendations using Deep Neural Networks | News recommender systems are aimed to personalize users experiences and help
them to discover relevant articles from a large and dynamic search space.
Therefore, news domain is a challenging scenario for recommendations, due to
its sparse user profiling, fast growing number of items, accelerated item's
value decay, and users preferences dynamic shift. Some promising results have
been recently achieved by the usage of Deep Learning techniques on Recommender
Systems, specially for item's feature extraction and for session-based
recommendations with Recurrent Neural Networks. In this paper, it is proposed
an instantiation of the CHAMELEON -- a Deep Learning Meta-Architecture for News
Recommender Systems. This architecture is composed of two modules, the first
responsible to learn news articles representations, based on their text and
metadata, and the second module aimed to provide session-based recommendations
using Recurrent Neural Networks. The recommendation task addressed in this work
is next-item prediction for users sessions: "what is the next most likely
article a user might read in a session?" Users sessions context is leveraged by
the architecture to provide additional information in such extreme cold-start
scenario of news recommendation. Users' behavior and item features are both
merged in an hybrid recommendation approach. A temporal offline evaluation
method is also proposed as a complementary contribution, for a more realistic
evaluation of such task, considering dynamic factors that affect global
readership interests like popularity, recency, and seasonality. Experiments
with an extensive number of session-based recommendation methods were performed
and the proposed instantiation of CHAMELEON meta-architecture obtained a
significant relative improvement in top-n accuracy and ranking metrics (10% on
Hit Rate and 13% on MRR) over the best benchmark methods. | http://arxiv.org/abs/1808.00076v3 | http://arxiv.org/pdf/1808.00076v3.pdf | null | [
"Gabriel de Souza P. Moreira",
"Felipe Ferreira",
"Adilson Marques da Cunha"
] | [
"News Recommendation",
"Recommendation Systems",
"Session-Based Recommendations"
] | 1,532,995,200,000 | [] | 166,734 |
254,403 | https://paperswithcode.com/paper/are-factuality-checkers-reliable-adversarial | null | Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization | With the continuous upgrading of the summarization systems driven by deep neural networks, researchers have higher requirements on the quality of the generated summaries, which should be not only fluent and informative but also factually correct. As a result, the field of factual evaluation has developed rapidly recently. Despite its initial progress in evaluating generated summaries, the meta-evaluation methodologies of factuality metrics are limited in their opacity, leading to the insufficient understanding of factuality metrics’ relative advantages and their applicability. In this paper, we present an adversarial meta-evaluation methodology that allows us to (i) diagnose the fine-grained strengths and weaknesses of 6 existing top-performing metrics over 24 diagnostic test datasets, (ii) search for directions for further improvement by data augmentation. Our observations from this work motivate us to propose several calls for future research. We make all codes, diagnostic test datasets, trained factuality models available: https://github.com/zide05/AdvFact. | https://aclanthology.org/2021.findings-emnlp.179 | https://aclanthology.org/2021.findings-emnlp.179.pdf | Findings (EMNLP) 2021 11 | [
"Yiran Chen",
"PengFei Liu",
"Xipeng Qiu"
] | [
"Data Augmentation"
] | 1,635,724,800,000 | [] | 110,904 |
169,201 | https://paperswithcode.com/paper/block-term-tensor-neural-networks | 2010.04963 | Block-term Tensor Neural Networks | Deep neural networks (DNNs) have achieved outstanding performance in a wide range of applications, e.g., image classification, natural language processing, etc. Despite the good performance, the huge number of parameters in DNNs brings challenges to efficient training of DNNs and also their deployment in low-end devices with limited computing resources. In this paper, we explore the correlations in the weight matrices, and approximate the weight matrices with the low-rank block-term tensors. We name the new corresponding structure as block-term tensor layers (BT-layers), which can be easily adapted to neural network models, such as CNNs and RNNs. In particular, the inputs and the outputs in BT-layers are reshaped into low-dimensional high-order tensors with a similar or improved representation power. Sufficient experiments have demonstrated that BT-layers in CNNs and RNNs can achieve a very large compression ratio on the number of parameters while preserving or improving the representation power of the original DNNs. | https://arxiv.org/abs/2010.04963v2 | https://arxiv.org/pdf/2010.04963v2.pdf | null | [
"Jinmian Ye",
"Guangxi Li",
"Di Chen",
"Haiqin Yang",
"Shandian Zhe",
"Zenglin Xu"
] | [
"Image Classification"
] | 1,602,288,000,000 | [] | 150,066 |
244,768 | https://paperswithcode.com/paper/aggregation-with-feature-detection | null | Aggregation With Feature Detection | Aggregating features from different depths of a network is widely adopted to improve the network capability. Lots of modern architectures are equipped with skip connections, which actually makes the feature aggregation happen in all these networks. Since different features tell different semantic meanings, there are inconsistencies and incompatibilities to be solved. However, existing works naively blend deep features via element-wise summation or concatenation with a convolution behind. Better feature aggregation method beyond summation or concatenation is rarely explored. In this paper, given two layers of features to be aggregated together, we first detect and identify where and what needs to be updated in one layer, then replace the feature at the identified location with the information of the other layer. This process, which we call DEtect-rePLAce (DEPLA), enables us to avoid inconsistent patterns while keeping useful information in the merged outputs. Experimental results demonstrate our method largely boosts multiple baselines e.g. ResNet, FishNet and FPN on three major vision tasks including ImageNet classification, MS COCO object detection and instance segmentation. | http://openaccess.thecvf.com//content/ICCV2021/html/Sun_Aggregation_With_Feature_Detection_ICCV_2021_paper.html | http://openaccess.thecvf.com//content/ICCV2021/papers/Sun_Aggregation_With_Feature_Detection_ICCV_2021_paper.pdf | ICCV 2021 10 | [
"Shuyang Sun",
"Xiaoyu Yue",
"Xiaojuan Qi",
"Wanli Ouyang",
"Victor Adrian Prisacariu",
"Philip H.S. Torr"
] | [
"Instance Segmentation",
"Object Detection",
"Object Detection",
"Semantic Segmentation"
] | 1,609,459,200,000 | [
{
"code_snippet_url": "",
"description": "**Average Pooling** is a pooling operation that calculates the average value for patches of a feature map, and uses it to create a downsampled (pooled) feature map. It is usually used after a convolutional layer. It adds a small amount of translation invariance - me... | 39,332 |
186,426 | https://paperswithcode.com/paper/towards-adversarial-learning-of-speaker | 1903.09606 | Towards adversarial learning of speaker-invariant representation for speech emotion recognition | Speech emotion recognition (SER) has attracted great attention in recent
years due to the high demand for emotionally intelligent speech interfaces.
Deriving speaker-invariant representations for speech emotion recognition is
crucial. In this paper, we propose to apply adversarial training to SER to
learn speaker-invariant representations. Our model consists of three parts: a
representation learning sub-network with time-delay neural network (TDNN) and
LSTM with statistical pooling, an emotion classification network and a speaker
classification network. Both the emotion and speaker classification network
take the output of the representation learning network as input. Two training
strategies are employed: one based on domain adversarial training (DAT) and the
other one based on cross-gradient training (CGT). Besides the conventional data
set, we also evaluate our proposed models on a much larger publicly available
emotion data set with 250 speakers. Evaluation results show that on IEMOCAP,
DAT and CGT provides 5.6% and 7.4% improvement respectively, over a baseline
system without speaker-invariant representation learning on 5-fold cross
validation. On the larger emotion data set, while CGT fails to yield better
results than baseline, DAT can still provide 9.8% relative improvement on a
standalone test set. | http://arxiv.org/abs/1903.09606v1 | http://arxiv.org/pdf/1903.09606v1.pdf | null | [] | [
"Classification",
"Emotion Classification",
"Emotion Recognition",
"Representation Learning",
"Speech Emotion Recognition"
] | 1,553,212,800,000 | [] | 91,257 |
110,612 | https://paperswithcode.com/paper/chinese-relation-extraction-with-multi | null | Chinese Relation Extraction with Multi-Grained Information and External Linguistic Knowledge | Chinese relation extraction is conducted using neural networks with either character-based or word-based inputs, and most existing methods typically suffer from segmentation errors and ambiguity of polysemy. To address the issues, we propose a multi-grained lattice framework (MG lattice) for Chinese relation extraction to take advantage of multi-grained language information and external linguistic knowledge. In this framework, (1) we incorporate word-level information into character sequence inputs so that segmentation errors can be avoided. (2) We also model multiple senses of polysemous words with the help of external linguistic knowledge, so as to alleviate polysemy ambiguity. Experiments on three real-world datasets in distinct domains show consistent and significant superiority and robustness of our model, as compared with other baselines. We will release the source code of this paper in the future. | https://aclanthology.org/P19-1430 | https://aclanthology.org/P19-1430.pdf | ACL 2019 7 | [
"Ziran Li",
"Ning Ding",
"Zhiyuan Liu",
"Hai-Tao Zheng",
"Ying Shen"
] | [
"Relation Extraction"
] | 1,561,939,200,000 | [] | 122,862 |
98,124 | https://paperswithcode.com/paper/transformable-bottleneck-networks | 1904.06458 | Transformable Bottleneck Networks | We propose a novel approach to performing fine-grained 3D manipulation of image content via a convolutional neural network, which we call the Transformable Bottleneck Network (TBN). It applies given spatial transformations directly to a volumetric bottleneck within our encoder-bottleneck-decoder architecture. Multi-view supervision encourages the network to learn to spatially disentangle the feature space within the bottleneck. The resulting spatial structure can be manipulated with arbitrary spatial transformations. We demonstrate the efficacy of TBNs for novel view synthesis, achieving state-of-the-art results on a challenging benchmark. We demonstrate that the bottlenecks produced by networks trained for this task contain meaningful spatial structure that allows us to intuitively perform a variety of image manipulations in 3D, well beyond the rigid transformations seen during training. These manipulations include non-uniform scaling, non-rigid warping, and combining content from different images. Finally, we extract explicit 3D structure from the bottleneck, performing impressive 3D reconstruction from a single input image. | https://arxiv.org/abs/1904.06458v5 | https://arxiv.org/pdf/1904.06458v5.pdf | ICCV 2019 10 | [
"Kyle Olszewski",
"Sergey Tulyakov",
"Oliver Woodford",
"Hao Li",
"Linjie Luo"
] | [
"3D Reconstruction",
"Novel View Synthesis"
] | 1,555,113,600,000 | [] | 120,802 |
107,961 | https://paperswithcode.com/paper/volmap-a-real-time-model-for-semantic | 1906.11873 | VolMap: A Real-time Model for Semantic Segmentation of a LiDAR surrounding view | This paper introduces VolMap, a real-time approach for the semantic segmentation of a 3D LiDAR surrounding view system in autonomous vehicles. We designed an optimized deep convolution neural network that can accurately segment the point cloud produced by a 360\degree{} LiDAR setup, where the input consists of a volumetric bird-eye view with LiDAR height layers used as input channels. We further investigated the usage of multi-LiDAR setup and its effect on the performance of the semantic segmentation task. Our evaluations are carried out on a large scale 3D object detection benchmark containing a LiDAR cocoon setup, along with KITTI dataset, where the per-point segmentation labels are derived from 3D bounding boxes. We show that VolMap achieved an excellent balance between high accuracy and real-time running on CPU. | https://arxiv.org/abs/1906.11873v1 | https://arxiv.org/pdf/1906.11873v1.pdf | null | [
"Hager Radi",
"Waleed Ali"
] | [
"3D Object Detection",
"Autonomous Vehicles",
"Object Detection",
"Object Detection",
"Semantic Segmentation"
] | 1,560,297,600,000 | [
{
"code_snippet_url": null,
"description": "A **convolution** is a type of matrix operation, consisting of a kernel, a small matrix of weights, that slides over input data performing element-wise multiplication with the part of the input it is on, then summing the results into an output.\r\n\r\nIntuitively,... | 71,378 |
123,059 | https://paperswithcode.com/paper/using-dynamic-embeddings-to-improve-static | 1911.02929 | How Can BERT Help Lexical Semantics Tasks? | Contextualized embeddings such as BERT can serve as strong input representations to NLP tasks, outperforming their static embeddings counterparts such as skip-gram, CBOW and GloVe. However, such embeddings are dynamic, calculated according to a sentence-level context, which limits their use in lexical semantics tasks. We address this issue by making use of dynamic embeddings as word representations in training static embeddings, thereby leveraging their strong representation power for disambiguating context information. Results show that this method leads to improvements over traditional static embeddings on a range of lexical semantics tasks, obtaining the best reported results on seven datasets. | https://arxiv.org/abs/1911.02929v2 | https://arxiv.org/pdf/1911.02929v2.pdf | null | [
"Yile Wang",
"Leyang Cui",
"Yue Zhang"
] | [
"Word Embeddings"
] | 1,573,084,800,000 | [
{
"code_snippet_url": "",
"description": "**GloVe Embeddings** are a type of word embedding that encode the co-occurrence probability ratio between two words as vector differences. GloVe uses a weighted least squares objective $J$ that minimizes the difference between the dot product of the vectors of two w... | 135,696 |
307,643 | https://paperswithcode.com/paper/funqg-molecular-representation-learning-via | 2207.08597 | FunQG: Molecular Representation Learning Via Quotient Graphs | Learning expressive molecular representations is crucial to facilitate the accurate prediction of molecular properties. Despite the significant advancement of graph neural networks (GNNs) in molecular representation learning, they generally face limitations such as neighbors-explosion, under-reaching, over-smoothing, and over-squashing. Also, GNNs usually have high computational complexity because of the large-scale number of parameters. Typically, such limitations emerge or increase when facing relatively large-size graphs or using a deeper GNN model architecture. An idea to overcome these problems is to simplify a molecular graph into a small, rich, and informative one, which is more efficient and less challenging to train GNNs. To this end, we propose a novel molecular graph coarsening framework named FunQG utilizing Functional groups, as influential building blocks of a molecule to determine its properties, based on a graph-theoretic concept called Quotient Graph. By experiments, we show that the resulting informative graphs are much smaller than the molecular graphs and thus are good candidates for training GNNs. We apply the FunQG on popular molecular property prediction benchmarks and then compare the performance of a GNN architecture on the obtained datasets with several state-of-the-art baselines on the original datasets. By experiments, this method significantly outperforms previous baselines on various datasets, besides its dramatic reduction in the number of parameters and low computational complexity. Therefore, the FunQG can be used as a simple, cost-effective, and robust method for solving the molecular representation learning problem. | https://arxiv.org/abs/2207.08597v1 | https://arxiv.org/pdf/2207.08597v1.pdf | null | [
"Hossein Hajiabolhassan",
"Zahra Taheri",
"Ali Hojatnia",
"Yavar Taheri Yeganeh"
] | [
"Molecular Property Prediction",
"Representation Learning"
] | 1,658,102,400,000 | [] | 54,202 |
182,790 | https://paperswithcode.com/paper/mosaicked-multispectral-image-compression | 1801.03577 | Mosaicked multispectral image compression based on inter- and intra-band correlation | Multispectral imaging has been utilized in many fields, but the cost of
capturing and storing image data is still high. Single-sensor cameras with
multispectral filter arrays can reduce the cost of capturing images at the
expense of slightly lower image quality. When multispectral filter arrays are
used, conventional multispectral image compression methods can be applied after
interpolation, but the compressed image data after interpolation has some
redundancy because the interpolated data are computed from the captured raw
data. In this paper, we propose an efficient image compression method for
single-sensor multispectral cameras. The proposed method encodes the captured
multispectral data before interpolation. We also propose a new spectral
transform method for the compression of mosaicked multispectral images. This
transform is designed by considering the filter arrangement and the spectral
sensitivities of a multispectral filter array. The experimental results show
that the proposed method achieves a higher peak signal-to-noise ratio at higher
bit rates than a conventional compression method that encodes a multispectral
image after interpolation, e.g., 3-dB gain over conventional compression when
coding at rates of over 0.1 bit/pixel/bands. | http://arxiv.org/abs/1801.03577v1 | http://arxiv.org/pdf/1801.03577v1.pdf | null | [] | [
"Image Compression"
] | 1,515,542,400,000 | [] | 149,774 |
98,226 | https://paperswithcode.com/paper/swtvm-exploring-the-automated-compilation-for | 1904.07404 | swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor | The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79x on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor. | https://arxiv.org/abs/1904.07404v3 | https://arxiv.org/pdf/1904.07404v3.pdf | null | [
"Mingzhen Li",
"Changxi Liu",
"Jianjin Liao",
"Xuegui Zheng",
"Hailong Yang",
"Rujun Sun",
"Jun Xu",
"Lin Gan",
"Guangwen Yang",
"Zhongzhi Luan",
"Depei Qian"
] | [
"Code Generation"
] | 1,555,372,800,000 | [
{
"code_snippet_url": "https://www.healthnutra.org/es/maxup/",
"description": "A **1 x 1 Convolution** is a [convolution](https://paperswithcode.com/method/convolution) with some special properties in that it can be used for dimensionality reduction, efficient low dimensional embeddings, and applying non-li... | 62,359 |
197,959 | https://paperswithcode.com/paper/instantaneous-psd-estimation-for-speech | 2007.00542 | Instantaneous PSD Estimation for Speech Enhancement based on Generalized Principal Components | Power spectral density (PSD) estimates of various microphone signal components are essential to many speech enhancement procedures. As speech is highly non-nonstationary, performance improvements may be gained by maintaining time-variations in PSD estimates. In this paper, we propose an instantaneous PSD estimation approach based on generalized principal components. Similarly to other eigenspace-based PSD estimation approaches, we rely on recursive averaging in order to obtain a microphone signal correlation matrix estimate to be decomposed. However, instead of estimating the PSDs directly from the temporally smooth generalized eigenvalues of this matrix, yielding temporally smooth PSD estimates, we propose to estimate the PSDs from newly defined instantaneous generalized eigenvalues, yielding instantaneous PSD estimates. The instantaneous generalized eigenvalues are defined from the generalized principal components, i.e. a generalized eigenvector-based transform of the microphone signals. We further show that the smooth generalized eigenvalues can be understood as a recursive average of the instantaneous generalized eigenvalues. Simulation results comparing the multi-channel Wiener filter (MWF) with smooth and instantaneous PSD estimates indicate better speech enhancement performance for the latter. A MATLAB implementation is available online. | https://arxiv.org/abs/2007.00542v1 | https://arxiv.org/pdf/2007.00542v1.pdf | null | [] | [
"Speech Enhancement"
] | 1,593,561,600,000 | [] | 166,124 |
300,148 | https://paperswithcode.com/paper/transformer-based-urdu-handwritten-text | 2206.04575 | Transformer based Urdu Handwritten Text Optical Character Reader | Extracting Handwritten text is one of the most important components of digitizing information and making it available for large scale setting. Handwriting Optical Character Reader (OCR) is a research problem in computer vision and natural language processing computing, and a lot of work has been done for English, but unfortunately, very little work has been done for low resourced languages such as Urdu. Urdu language script is very difficult because of its cursive nature and change of shape of characters based on it's relative position, therefore, a need arises to propose a model which can understand complex features and generalize it for every kind of handwriting style. In this work, we propose a transformer based Urdu Handwritten text extraction model. As transformers have been very successful in Natural Language Understanding task, we explore them further to understand complex Urdu Handwriting. | https://arxiv.org/abs/2206.04575v1 | https://arxiv.org/pdf/2206.04575v1.pdf | null | [
"Mohammad Daniyal Shaiq",
"Musa Dildar Ahmed Cheema",
"Ali Kamal"
] | [
"Natural Language Understanding",
"Optical Character Recognition"
] | 1,654,732,800,000 | [] | 884 |
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