深度学习论文

蜻蜓点论文， paperskim, deep learning papers. the table of content of all my videos

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论文名	Bilibili	Youtube	Arxiv	博客	序号
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VAEBM: Variational Autoencoders and Energy-based Models					174
On Separability of Self-Supervised Representations					173
Revisiting Knowledge Distillation via Label Smoothing Regularization					172
Regularizing Class-wise Predictions via Self-knowledge Distillation					171
Be Your Own Teacher: Improve CNN via Self Distillation					170
以下的B站链接全部失效， 被我删了
Bayesian Deep Learning and a Probabilistic Perspective of Generalization					169
Rethink Image Mixture for Unsupervised Visual Representation Learning					168
FixMatch: Semi-Supervised Learning with Consistency and Confidence					167
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring					166
MiCE: Mixture of Contrastive Experts for Unsupervised Image Clustering					165
Learn Representation via Information Maximizing Self-Augmented Training					164
Supporting Clustering with Contrastive Learning					163
Unsupervised Multi-hop Question Answering by Question Generation					162
Perceiver: General Perception with Iterative Attention					161
Joint EBM Training for Better Calibrated NLU Models					160
A Unified Energy-Based Framework for Unsupervised Learning					159
Energy-Based Models for Deep Probabilistic Regression					158
Contrastive Learning Inverts the Data Generating Process					157
Asymmetric Loss For Multi-Label Classification					156
Computation-Efficient Knowledge Distillation by Uncertainty-Aware Mixup					155
Knowledge Distillation Meets Self-Supervision					154
Feature Projection for Improved Text Classification					153
Improve Joint Train of Inference Net and Structure Predict EnergyNet					152
BertGCN: Transductive Text Classification by Combining GCN and Bert					151
The Authors Matter Understand Mitigate Implicit Bias in text classification					150
Learning Approximate Inference Networks for Structured Prediction					149
End-to-End Learning for Structured Prediction Energy Networks					148
Revisiting Unsupervised Relation Extraction					147
Sentence Meta-Embeddings for Unsupervised Semantic Textual Similarity					146
X-Class: Text Classification with Extremely Weak Supervision					145
Paint by Word					144
Shape-Texture Debiased Neural Network Training					143
Contrastive Learning through Alignment and Uniformity on the Hypersphere					142
Deep INFOMAX representation mutual information estimation maximization					141
SimCSE: Simple Contrastive Learning of Sentence Embeddings					140
IMOJIE Iterative Memory-Based Joint Open Information Extraction					139
Trash is Treasure Resisting Adversarial Examples by Adversarial Examples					138
Enhancing Adversarial Defense by k-Winners-Take-All					137
On Adaptive Attacks to Adversarial Example Defenses					136
Knowledge distillation via softmax regression representation learning					135
Revisiting Locally Supervised Learning Alternative to End-to-end Training					134
Putting An End to End-to-End Gradient-Isolated Learning of Representations					133
防御defense变分自编码器					132
Triple Wins Accuracy Robustness Efficiency by Input-adaptive Inference					131
Using latent space regression to analyze leverage compositionality in GANs					130
Theoretically(没看) Principled Trade-off between Robustness and Accuracy					129
Representation learning with contrastive predictive coding					128
Learning Representations for Time Series Clustering					127
Stochastic Security: Adversarial Defense Using Long-Run Dynamics of EBM					126
Improving Adversarial Robustness via Channel-wise Activation Suppressing					125
Likelihood Landscapes: A Unifying Principle Behind Adversarial Defenses					124
Barlow Twins: Self-Supervised Learning via Redundancy Reduction					123
Geometry-Aware Instance-Reweighted Adversarial Training					122
A Closer Look at Accuracy vs Robustness					121
Unsupervised Clustering of Seismic Signals 地震波 using autoencoders					120
Towards the first adversarially robust neural network model on MNIST					119
PGD对抗训练 Towards Deep Learning Models Resistant to Adversarial Attacks					118
Denoising Diffusion Probabilistic Models					117
Deep Unsupervised Learning using Nonequilibrium Thermodynamics					116
Variational Inference with Normalizing Flows					115
CutMix Regularization Strategy with Localizable Features					114
Clustering-friendly Representation Learning Feature Decorrelate					113
Energy-based Out-of-distribution Detection					112
High-Performance Large-Scale Image Recognition Without Normalization					111
Characterizing signal propagation in unnormalized ResNets					110
Concept Learners for Few-Shot Learning					109
Image Generation by Minimize Frechet Distance in Discriminator feature space					108
Learning Non-Convergent Non-Persistent Short-Run MCMC to EBM					107
Concept Whitening for Interpretable Image Recognition					106
Loss Landscape Sightseeing with Multi-Point Optimization					105
Loss Surfaces, Mode Connectivity, and Fast Ensembling of DNNs					104
Essentially No Barriers in Neural Network Energy Landscape					103
Visualizing the Loss Landscape of Neural Nets					102
Self-training for Few-shot Transfer Across Extreme Task Differences					101
Darts: Differentiable architecture search					100
Architecture Search Space in Neural Architecture Search(NAS)					99
Free Lunch for Few-shot Learning: Distribution Calibration					98
Online Deep Clustering for Unsupervised Representation Learning					97
Coarse-to-Fine Pre-training for Named Entity Recognition					96
Unsupervised Domain Adaptation with Variational Information Bottleneck					95
A Unified MRC Framework for Named Entity Recognition					94
Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness					93
Super-Convergence: Very Fast Training of NN use large LR					92
Contrastive Clustering					91
Graph Contrastive Learning with Augmentations NIPS 2020 / Graph Contrastive Learning with Adaptive Augmentation WWW 2021/ GCC: Graph Contrastive Coding for Graph Neural Network ... KDD 2021	GNN and Contrastive Learning				90
Contrastive Representation Distillation					89
Spectral Norm Regularization for Improving the Generalizability of NN					88
UDA: Unsupervised Data Augmentation for Consistency Training					87
Simplify the Usage of Lexicon in Chinese NER					86
Chinese NER Using Lattice LSTM					85
Uncertainty-aware Self-training for Few-shot Text Classification					84
Concept Learning with Energy-Based Models					83
Training data-efficient image transformers distillation through attention					82
Adversarial Training Methods for Semi-Supervised Text Classification					81
Delta-training Semi-Supervised Text Classification with word embedding					80
On the Anatomy of MCMC-Based Maximum Likelihood Learning of EBMs					79
Unsupervised Deep Embedding for Clustering Analysis					78
Relation of Relation Learning Network for Sentence Semantic Matching					77
Contextual Parameter Generation for Universal Neural Machine Translation					76
Exploring Simple Siamese Representation Learning					75
Contextual Parameter Generation for Knowledge Graph Link Prediction					74
Robustness May Be at Odds with Accuracy					73
Learning with Multiplicative Perturbations					72
When Do Curricula Work？					71
Self-Supervised Contrastive Learning with Adversarial Examples					70
Supervised Contrastive Learning					69
A Note on the Inception Score and FID					68
Hierarchical Semantic Aggregation for Contrastive Representation Learning					67
Syntactic and Semantic-driven Learning for Open Information Extraction					66
Text Classification with Negative Supervision					65
CESI Canonicalizing Open Knowledge Bases by Embeddings and Side Information					64
CaRe: Open Knowledge Graph Embedding					63
No MCMC for me, Amortized sampling for fast and stable training of EBMs					62
Knowledge Graph Embedding Based Question Answering					61
VAT Virtual Adversarial Training for regularization semi-supervised learn					60
CNN-Generated Images Are Surprisingly Easy to Spot.. For Now					59
Graph Agreement Models for Semi-Supervised Learning					58
Be More with Less: Hypergraph Attention Networks for Inductive 文本分类					57
Text Level Graph Neural Network for Text Classification					56
Graph Convolutional Networks for Text Classification					55
Learning sparse neural networks through L0 regularization					54
BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis					53
On the steerability of generative adversarial networks					52
What Makes for Good Views for Contrastive Learning					51
Viewmaker Networks Learning Views for Unsupervised Representation Learning					50
Auto-Encoding Variational Bayes					49
Adversarial Examples Improve Image Recognition					48
Stochastic Weight Averaging for Generalization					47
There Are Many Consistent Explanations Of Unlabeled Data					46
Interpretable Convolutional Neural Networks					45
Understanding Black-box Predictions via Influence Functions					44
Adversarial Examples Are Not Bugs, They Are Features					43
You Only Propagate Once Accelerating AT via Maximal Principle					42
Text Classification Using Label Names Only A LM self-training way					41
10篇softmax，CrossEntropyLoss替代方法论文合集					40
Cyclical Stochastic Gradient MCMC and snapshot ensemble					39
Unsupervised Feature Learning via Non-Parametric Instance Discrimination					38
An Image is Worth 16x16 Words Transformers for Image Recognition at Scale					37
Training independent subnetworks for robust prediction					36
Active Learning for CNNs: A Core-Set Approach					35
SimCLR A Simple Framework for Contrastive Learning of Visual Representation					34
UNITER: UNiversal Image-TExt Representation Learning					33
Image Synthesis with a Single (Robust) Classifier					32
Set Transformer A Framework for Attention-based Permutation-Invariant NN					31
Consistency Regularization in Semi-Supervised Learning					30
Did the model understand the question					29
Rethinking Feature Distribution for Loss Functions in Image Classification					28
Bootstrap your own latent: A new way to self supervised learning					27
Hybrid Discriminative-Generative Training via Contrastive Learning(EBMs)					26
A Multimodal Translation-Based Approach for Knowledge Graph Representation (ACL 2018)					25
Deep Bayesian Active Learning with Image Data (ICML 2017) The power of ensembles for active learning in image classification (CVPR 2018)	Bilibili				24
SCAN Learnrnning to Classify Images without Labels (ECCV 2020)					23
Unsupervised Question Answering by Cloze Translation (ACL 2019)					22
Phrase-Based & Neural Unsupervised Machine Translation (EMNLP 2018)					21
MixUp as Locally Linear Out-Of-Manifold Regularization (AAAI 2019)					20
Manifold Mixup: Better Representations by Interpolating Hidden States ICML2019					19
Bag of Tricks for Image Classification with CNN					18
On Mixup Training Improved Calibration for DNN					17
BERT: Pre-training of Deep Bidirectional Transformers					16
Rationalizing Neural Predictions (EMNLP2016)					15
Attention is all you need, Transformer (NIPS 2017)					14
Learn To Pay Attention (ICLR 2018)					13
A Self-Training Method for MRC with Soft Evidence Extraction(ACL 2019)	Bilibili	Youtube			12
Deep Fool(CVPR2016) 和 Deep Defense(NIPS 2018)	Bilibili	youtube			11
R-Trans RNN Transformer Network for 中文机器阅理解(IEEE-Access)	Bilibili	youtube			10
一系列Energy-based models 能量模型论文摘要简介	Bilibili	Youtube			9
Implicit Generation and Modeling with EBM(NIPS 2019)	Bilibili	Youtube			8
MixMatch A Holistic Approach to Semi-supervised Learning(NIPS 2019)	Bilibili	Youtube	Arxiv		7
Obfuscated Gradients Give a False Sense of Security(ICML2017 best reward)	Bilibili	Youtube	Arxiv		6
Explaining and Harnessing Adversarial Examples(ICLR 2015)	Bilibili	Youtube	Arxiv		5
Imagenet-Trained CNNS are Biased Towards Texture(ICLR2018)	Bilibili	Youtube	Arxiv		4
Momentum Contrast for Unsupervised Visual Representation Learning(CVPR2020)	Bilibili	Youtube	Arxiv		3
Mixup: Beyond Empirical Risk Minimization(ICLR2018)	Bilibili	Youtube	Arxiv		2
Your Classifier is secretely an Energy Based Model(ICLR 2019)	Bilibili	Youtube	Arxiv		1（2020-08-19）

Paper List

可解释性

• Interpretable Convolutional Neural Networks
• Understanding Black-box Predictions via Influence Functions

Semi-Supervised Learning 半监督学习

• Regularization With Stochastic Transformations and Perturbations NIPS 2016
• Temporal Ensembling ICLR 2017
• Virtual Adversarial Training ICLR 2016
• Mean teachers are better role models: weight-averaged consistency targets NIPS 2017
• Realistic Evaluation of Deep Semi-SL NIPS 2018
• Deep co-training, ECCV 2018
• There Are Many Consistent Explanations Of Unlabeled Data why you should average ICLR 2019
• MixMatch A Holistic Approach to Semi-supervised Learning (NIPS 2019)

Softmax CrossEntropy Variants 交叉熵变种

• An Image is Worth 16x16 Words Transformers for Image Recognition at Scale
• Learn to Pay Attention (ICLR 2018)
• Large-Margin Softmax Loss for Convolutional Neural Networks ICML2016 https://arxiv.org/abs/1612.02295
• A Discriminative Feature Learning Approach for Deep Face Recognition ECCV 2016 https://link.springer.com/chapter/10.1007/978-3-319-46478-7_31
• Large Margin Deep Networks for Classification NIPS2018 https://arxiv.org/abs/1803.05598
• Rethinking Feature Distribution for Loss Functions in Image Classification CVPR 2018 http://arxiv.org/abs/1803.02988
• Max-Mahalanobis Linear Discriminant Analysis Networks http://arxiv.org/abs/1802.09308 ICML2018
• Rethinking Softmax Cross-Entropy Loss for Adversarial Robustness ICLR 2020 https://arxiv.org/pdf/1905.10626.pdf
• Redesigning the Classification Layer by Randomizing the Class Representation Vectors ICLR2021 under review https://openreview.net/forum?id=6_FjMpi_ebO
• Rethinking Feature Discrimination and Polymerization for Large-scale Recognition NIPS 2017 Deep Learning Workshop https://arxiv.org/abs/1710.00870
• Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination http://arxiv.org/abs/1805.01978 CVPR 2018
• RBF-Softmax: Learning Deep Representative Prototypes with Radial Basis Function Softmax, ECCV 2020

Active Learning 主动学习

• Active Learning for CNNs: A Core-Set Approach
• Deep Bayesian Active Learning with Image Data (ICML 2017)
• The power of ensembles for active learning in image classification (CVPR 2018)

多模态

• UNITER: UNiversal Image-TExt Representation Learning
• A Multimodal Translation-Based Approach for Knowledge Graph Representation (ACL 2018)

Transfoormer

• Attention is All you need (NIPS 2017)
• BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

稳健性 Robustness

• Explaining and Harnessing Adversarial Examples (ICLR 2015)
• Deep Fool (CVPR2016)
• Deep Defense (NIPS 2018)
• Obfuscated Gradients Give a False Sense of Security (ICML2017 best reward)
• Adversarial Examples Are Not Bugs, They Are Features
• Image Synthesis with a Single (Robust) Classifier
• Adversarial Examples Improve Image Recognition
• You Only Propagate Once Accelerating AT via Maximal Principle

QA/MRC 机器阅读理解和问答

• R-Trans RNN Transformer Network for 中文机器阅读理解 (IEEE-Access)
• A Self-Training Method for MRC with Soft Evidence Extraction (ACL 2019)
• Did the model understand the question
• Rationalizing Neural Predictions (EMNLP2016)

图神经网络

• Graph Agreement Models for Semi-Supervised Learning
• Be More with Less: Hypergraph Attention Networks for Inductive 文本分类
• Text Level Graph Neural Network for Text Classification
• Graph Convolutional Networks for Text Classification

生成模型

GAN 生成式对抗网络

• BigGAN: Large Scale GAN Training for High Fidelity Natural Image Synthesis
• On the steerability of generative adversarial networks

自编码器

• Auto-Encoding Variational Bayes

分析

• CNN-Generated Images Are Surprisingly Easy to Spot.. For Now

神经网络稀疏化

• Learning sparse neural networks through L0 regularization

文本分类

• Be More with Less: Hypergraph Attention Networks for Inductive 文本分类
• Text Level Graph Neural Network for Text Classification
• Graph Convolutional Networks for Text Classification

无监督学习 Unsupervised Learning

• Unsupervised Question Answering by Cloze Translation (ACL 2019)
• Phrase-Based & Neural Unsupervised Machine Translation (EMNLP 2018)
• SCAN Learnrnning to Classify Images without Labels (ECCV 2020)
• Text Classification Using Label Names Only A LM self-training way

Contrastive Learning 对比学习

• Unsupervised Feature Learning via Non-Parametric Instance Discrimination
• Momentum Contrast for Unsupervised Visual Representation Learning (CVPR2020)
• SimCLR A Simple Framework for Contrastive Learning of Visual Representation
• Bootstrap your own latent: A new way to self supervised learning
• Hybrid Discriminative-Generative Training via Contrastive Learning(EBMs)
• What Makes for Good Views for Contrastive Learning
• Viewmaker Networks Learning Views for Unsupervised Representation Learning

EBM 能量模型

• Implicit Generation and Modeling with EBM (NIPS 2019)
• Your Classifier is secretely an Energy Based Model (ICLR 2019)
• Hybrid Discriminative-Generative Training via Contrastive Learning(EBMs)

Tricks 技巧

• Bag of Tricks for Image Classification with CNN

MixUp 数据融合

• Mixup: Beyond Empirical Risk Minimization (ICLR2018)
• MixUp as Locally Linear Out-Of-Manifold Regularization (AAAI 2019)
• Manifold Mixup: Better Representations by Interpolating Hidden States ICML2019
• On Mixup Training Improved Calibration

集成学习

• Cyclical Stochastic Gradient MCMC
• snapshot ensemble
• Training independent subnetworks for robust prediction
• Averaging Weights Leads to Wider Optima and Better Generalization Arxiv

其他

• Set Transformer: A Framework for Attention-based Permutation-Invariant NN
• Imagenet-Trained CNNS are Biased Towards Texture (ICLR2018)