No 1. 《A survey on Semi-, Self- and Unsupervised Learning for Image Classification》
No 2. 《A Closer Look at Accuracy vs. Robustness》
No 3. *《Big Bird: Transformers for Longer Sequences》
No 4. 《Natural Graph Networks》
No 5. 《Unsupervised Learning of Image Segmentation Based on Differentiable Feature Clustering》
No 6. 《FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning》
No 7. *《The Representation Theory of Neural Networks》
No 8. 《Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild》
No 9. 《3D Human Shape and Pose from a Single Low-Resolution Image with Self-Supervised Learning》
No 10. 《Style is a Distribution of Features》
No 11. *《Whole-Body Human Pose Estimation in the Wild》
No 12. *《Towards Learning Convolutions from Scratch》
No 13. 《Deformable Style Transfer》
No 14. *《Accelerating Deep Learning Applications in Space》
No 15. 《TSIT: A Simple and Versatile Framework for Image-to-Image Translation》
No 16. *《Contact and Human Dynamics from Monocular Video》
No 17. 《SliceOut: Training Transformers and CNNs faster while using less memory》
No 18. *《The Surprising Effectiveness of Linear Unsupervised Image-to-Image Translation》
No 19. 《Rewriting a Deep Generative Model》
No 20. 《Translate the Facial Regions You Like Using Region-Wise Normalization》
No 21. *《Contrastive Learning for Unpaired Image-to-Image Translation》
No 22. 《Smooth-AP: Smoothing the Path Towards Large-Scale Image Retrieval》
No 23. *《Coarse Graining Molecular Dynamics with Graph Neural Networks》
No 24. 《Flower: A Friendly Federated Learning Research Framework》
No 25. *《IBM Federated Learning: an Enterprise Framework White Paper V0.1》
No 26. 《Music FaderNets: Controllable Music Generation Based On High-Level Features via Low-Level Feature Modelling》
No 27. 《Learning RGB-D Feature Embeddings for Unseen Object Instance Segmentation》
No 28. 《NormalGAN: Learning Detailed 3D Human from a Single RGB-D Image》
No 29. blog:《Adversarial Robustness Through Local Lipschitzness | UCSD Machine Learning Group》
No 30. 《Online Invariance Selection for Local Feature Descriptors》