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| An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale | 12042 | ||
|---|---|---|---|
| A Simple Framework for Contrastive Learning of Visual Representations | 8476 | ||
| Language Models are Few-Shot Learners | 7903 | OpenAI | |
| YOLOv4: Optimal Speed and Accuracy of Object Detection | 7860 | Academia Sinica | |
| Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer. | 6362 | ||
| Momentum Contrast for Unsupervised Visual Representation Learning | 6060 | Meta | |
| End-to-End Object Detection with Transformers | 4998 | Meta, Paris Dauphine University | |
| Analyzing and Improving the Image Quality of StyleGAN | 3101 | Aalto University, NVIDIA | |
| EfficientDet: Scalable and Efficient Object Detection | 3081 | ||
| Advances and Open Problems in Federated Learning | 2921 | Australian National University, Carnegie Mellon University, Cornell University, Emory University, École Polytechnique Fédérale de Lausanne, Georgia Institute of Technology, Google, Hong Kong University of Science and Technology, INRIA, IT University of Copenhagen, MIT, Nanyang Technological University, Princeton University, Rutgers University, Stanford University, UC Berkeley, UC San Diego, University of Illinois Urbana-Champaign, University of Oulu, University of Pittsburgh, University of Southern California, University of Virginia, University of Warwick, University of Washington, University of Wisconsin-Madison | |
| Unsupervised Cross-lingual Representation Learning at Scale | 2857 | Meta | |
| Bootstrap your own latent: A new approach to self-supervised Learning | 2827 | DeepMind, Imperial College London | |
| Training data-efficient image transformers & distillation through attention | 2558 | Meta, Sorbonne University | |
| Random Erasing Data Augmentation. | 2453 | University of Technology Sydney, Xiamen University | |
| nuScenes: A Multimodal Dataset for Autonomous Driving | 2366 | nuTonomy | |
| NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis | 2283 | Google, UC Berkeley, UC San Diego | |
| ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators | 2142 | CIFAR, Google, Stanford University | |
| Improved protein structure prediction using potentials from deep learning | 2121 | DeepMind, Francis Crick Institute, University College London | |
| Transformers: State-of-the-Art Natural Language Processing | 2071 | Hugging Face | |
| Unsupervised Learning of Visual Features by Contrasting Cluster Assignments | 1847 | INRIA, Meta | |
| Supervised Contrastive Learning | 1835 | Boston University, Google, MIT, Snap Inc. | |
| Improved Baselines with Momentum Contrastive Learning | 1782 | Meta | |
| wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations | 1777 | Meta | |
| Exploring Simple Siamese Representation Learning | 1767 | Meta | |
| ECA-Net: Efficient Channel Attention for Deep Convolutional Neural Networks | 1713 | Dalian University of Technology, Harbin Institute of Technology, Tianjin University | |
| RandAugment: Practical Automated Data Augmentation with a Reduced Search Space | 1684 | ||
| Self-Training With Noisy Student Improves ImageNet Classification | 1674 | Carnegie Mellon University, Google | |
| Longformer: The Long-Document Transformer | 1650 | Allen Institute for Artificial Intelligence | |
| FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence | 1603 | ||
| Face2Face: Real-time Face Capture and Reenactment of RGB Videos | 1532 | Max Planck Institute for Informatics, Stanford University, University of Erlangen-Nuremberg | |
| Image Segmentation Using Deep Learning: A Survey | 1450 | Qualcomm, Snapchat, UC Los Angeles, University of Extremadura, University of Texas at Dallas, University of Waterloo | |
| Unsupervised Data Augmentation for Consistency Training | 1345 | Carnegie Mellon University, Google | |
| Big Self-Supervised Models are Strong Semi-Supervised Learners | 1314 | ||
| SpanBERT: Improving Pre-training by Representing and Predicting Spans | 1307 | Allen Institute for Artificial Intelligence, Meta, Princeton University, University of Washington | |
| Conformer: Convolution-augmented Transformer for Speech Recognition | 1214 | ||
| Scalability in Perception for Autonomous Driving: Waymo Open Dataset | 1209 | Google, Waymo | |
| Denoising Diffusion Probabilistic Models | 1206 | UC Berkeley | |
| Don't Stop Pretraining: Adapt Language Models to Domains and Tasks | 1150 | Allen Institute for Artificial Intelligence, University of Washington | |
| LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation | 1141 | Hefei University of Technology, Kuaishou Technology, National University of Singapore, University of Science and Technology of China | |
| Open Graph Benchmark: Datasets for Machine Learning on Graphs | 1112 | Harvard University, Microsoft, Stanford University, Technical University Dortmund | |
| Dense Passage Retrieval for Open-Domain Question Answering | 1106 | Meta, Princeton University, University of Washington | |
| SCAFFOLD: Stochastic Controlled Averaging for Federated Learning | 1052 | École Polytechnique Fédérale de Lausanne, Google, New York University | |
| Data-Efficient Image Recognition with Contrastive Predictive Coding | 1031 | DeepMind, UC Berkeley | |
| Stanza: A Python Natural Language Processing Toolkit for Many Human Languages | 1016 | Stanford University | |
| ResNeSt: Split-Attention Networks | 994 | Amazon, ByteDance, Meta, SenseTime, Snap Inc., UC Davis | |
| Self-Supervised Learning of Pretext-Invariant Representations | 993 | Meta | |
| Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks | 971 | Microsoft, University of Washington | |
| Implicit Neural Representations with Periodic Activation Functions | 963 | Stanford University | |
| TinyBERT: Distilling BERT for Natural Language Understanding | 954 | Huawei, Huazhong University of Science and Technology | |
| Big Bird: Transformers for Longer Sequences | 929 | ||
| BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning | 924 | Cornell University, Element, UC Berkeley, UC San Diego | |
| StarGAN v2: Diverse Image Synthesis for Multiple Domains | 916 | NAVER | |
| PV-RCNN: Point-Voxel Feature Set Abstraction for 3D Object Detection | 899 | Chinese Academy of Sciences, Chinese University of Hong Kong, National Laboratory of Pattern Recognition, SenseTime | |
| A Primer in BERTology: What we know about how BERT works | 892 | University of Copenhagen, University of Massachusetts Lowell | |
| RAFT: Recurrent All-Pairs Field Transforms for Optical Flow | 860 | Princeton University | |
| Multilingual Denoising Pre-training for Neural Machine Translation | 859 | Meta | |
| Bridging the Gap Between Anchor-Based and Anchor-Free Detection via Adaptive Training Sample Selection | 850 | Beijing University of Posts and Telecommunications, Chinese Academy of Sciences, National Laboratory of Pattern Recognition, University of Chinese Academy of Sciences, Westlake University | |
| Knowledge Distillation: A Survey | 850 | University of London, University of Sydney | |
| RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds | 848 | National University of Defense Technology, Sun Yat-sen University, University of Oxford | |
| SuperGlue: Learning Feature Matching With Graph Neural Networks | 839 | ETH Zurich, Magic Leap | |
| Generative Pretraining From Pixels | 838 | OpenAI | |
| Pre-trained models for natural language processing: A survey | 832 | Fudan University | |
| Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks | 811 | University of Tubingen | |
| Deep Learning for Person Re-identification: A Survey and Outlook | 806 | Beijing Institute of Technology, Inception Institute of AI, Salesforce, Singapore Management University, University of Surrey, Wuhan University | |
| Object-Contextual Representations for Semantic Segmentation | 800 | Chinese Academy of Sciences, Microsoft, University of Chinese Academy of Sciences | |
| Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains | 774 | Google, UC Berkeley, UC San Diego | |
| Scaled-YOLOv4: Scaling Cross Stage Partial Network | 758 | Academia Sinica, Intel, Providence University | |
| Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere | 755 | MIT | |
| Fast is better than free: Revisiting adversarial training | 744 | Bosch, Carnegie Mellon University | |
| Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting | 741 | Beihang University, Rutgers University, Sharjah Economic Development Department, UC Berkeley | |
| CodeBERT: A Pre-Trained Model for Programming and Natural Languages | 736 | Harbin Institute of Technology, Microsoft, Sun Yat-sen University | |
| Big Transfer (BiT): General Visual Representation Learning | 704 | ||
| Pre-Trained Image Processing Transformer | 703 | Huawei, Peking University, Peng Cheng Laboratory, University of Sydney | |
| Rethinking Attention with Performers | 697 | Alan Turing Institute, DeepMind, Google, University of Cambridge | |
| What makes for good views for contrastive learning | 686 | Brown University, Google, MIT | |
| Score-Based Generative Modeling through Stochastic Differential Equations | 684 | Google, Stanford University | |
| Graph Contrastive Learning with Augmentations | 682 | Google, Texas A&M University, University of Science and Technology of China, University of Texas at Austin | |
| Interpreting the Latent Space of GANs for Semantic Face Editing | 675 | Chinese University of Hong Kong | |
| Linformer: Self-Attention with Linear Complexity | 668 | Meta | |
| MaskGAN: Towards Diverse and Interactive Facial Image Manipulation | 653 | Chinese University of Hong Kong, SenseTime, University of Hong Kong | |
| Simple and Deep Graph Convolutional Networks | 653 | Alibaba Group, Fudan University, Renmin University of China | |
| REALM: Retrieval-Augmented Language Model Pre-Training | 640 | ||
| Single Path One-Shot Neural Architecture Search with Uniform Sampling | 627 | Hong Kong University of Science and Technology, Megvii, Tsinghua University | |
| Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing | 602 | Microsoft | |
| Tracking Objects as Points | 589 | Intel, University of Texas at Austin | |
| Adaptive Federated Optimization | 588 | ||
| Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inference | 581 | Ludwig Maximilian University of Munich, Sulzer GmbH | |
| Making Pre-trained Language Models Better Few-shot Learners | 576 | MIT, Princeton University | |
| Recipes for building an open-domain chatbot | 575 | Meta | |
| Meshed-Memory Transformer for Image Captioning | 572 | University of Modena and Reggio Emilia | |
| Unicoder-VL: A Universal Encoder for Vision and Language by Cross-Modal Pre-Training. | 568 | Microsoft, Peking University | |
| Transformers are RNNs: Fast Autoregressive Transformers with Linear Attention | 562 | École Polytechnique Fédérale de Lausanne, Idiap Research Institute, University of Geneva, University of Washington | |
| Learning to Simulate Complex Physics with Graph Networks | 547 | DeepMind, Stanford University | |
| Is BERT Really Robust? A Strong Baseline for Natural Language Attack on Text Classification and Entailment. | 546 | Agency for Science, Technology and Research, MIT, University of Hong Kong | |
| XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization | 536 | Carnegie Mellon University, DeepMind, Google | |
| On Adaptive Attacks to Adversarial Example Defenses | 535 | Google, MIT, Stanford University, University of Tubingen | |
| Circle Loss: A Unified Perspective of Pair Similarity Optimization | 534 | Australian National University, Beihang University, Megvii, Tsinghua University | |
| Explaining machine learning classifiers through diverse counterfactual explanations. | 527 | Microsoft, University of Colorado Boulder | |
| Efficient Transformers: A Survey | 524 | ||
| Exploring Self-attention for Image Recognition | 523 | Chinese University of Hong Kong, Intel |
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