编译|极市平台
1. star:9819|Weakly Supervised Disentanglement with Guarantees(弱监督学习)
论文:https://arxiv.org/pdf/1910.09772v2.pdf
代码:https://github.com/google-research/google-research/tree/master/weak_disentangle
2. star:9819|Measuring Compositional Generalization: A Comprehensive Method on Realistic Data(测量成分泛化)
论文:https://arxiv.org/pdf/1912.09713v1.pdf
代码:https://github.com/google-research/google-research/tree/master/cfq
3. star:9819|Meta-Learning without Memorization(元学习)
论文:https://arxiv.org/pdf/1912.03820v3.pdf
代码:https://github.com/google-research/google-research/tree/master/meta_learning_without_memorization
4. star:4977|U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation(图像翻译/无监督)
论文:https://arxiv.org/pdf/1907.10830v4.pdf
代码:https://github.com/taki0112/UGATIT
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5. star:2106|On the Variance of the Adaptive Learning Rate and Beyond
论文:https://arxiv.org/pdf/1908.03265v3.pdf
代码:https://github.com/LiyuanLucasLiu/RAdam
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6. star:1469|DiffTaichi: Differentiable Programming for Physical Simulation
论文:https://arxiv.org/pdf/1910.00935v3.pdf
代码:https://github.com/yuanming-hu/difftaichi
7. star:1018|Generative Models for Effective ML on Private, Decentralized Datasets
论文:https://arxiv.org/pdf/1911.06679v2.pdf
代码:https://github.com/tensorflow/federated/tree/master/tensorflow_federated/python/research/gans
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8. star:963|Behaviour Suite for Reinforcement Learning(强化学习)
论文:https://arxiv.org/pdf/1908.03568v3.pdf
代码:https://github.com/deepmind/bsuite
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9. star:534|Contrastive Representation Distillation(知识蒸馏)
论文:https://arxiv.org/pdf/1910.10699v2.pdf
代码:https://github.com/HobbitLong/RepDistiller
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10. star:516|On the Relationship between Self-Attention and Convolutional Layers(注意力机制)
论文:https://arxiv.org/pdf/1911.03584v2.pdf
代码:https://github.com/epfml/attention-cnn
11. star:469|AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
论文:https://arxiv.org/pdf/1912.02781v2.pdf
代码:https://github.com/rwightman/pytorch-image-models
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12. star:443|NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search(神经网络架构搜索)
论文:https://arxiv.org/pdf/2001.00326v2.pdf
代码:https://github.com/D-X-Y/NAS-Projects
13. star:393|Once for All: Train One Network and Specialize it for Efficient Deployment(神经网络训练)
论文:https://openreview.net/pdf?id=HylxE1HKwS
代码:https://github.com/mit-han-lab/once-for-all
14. star:246|BatchEnsemble: An Alternative Approach to Efficient Ensemble and Lifelong Learning(神经网络训练)
论文:https://arxiv.org/pdf/2002.06715v2.pdf
代码:https://github.com/google/edward2
15. star:243|FasterSeg: Searching for Faster Real-time Semantic Segmentation(语义分割)
论文:https://arxiv.org/pdf/1912.10917v2.pdf
代码:https://github.com/TAMU-VITA/FasterSeg
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16. star:213|Contrastive Learning of Structured World Models
论文:https://arxiv.org/pdf/1911.12247v2.pdf
代码:https://github.com/tkipf/c-swm
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17. star:191|Real or Not Real, that is the Question(GAN)
论文:https://arxiv.org/pdf/2002.05512v1.pdf
代码:https://github.com/kam1107/RealnessGAN
18. star:186|Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving(3D目标检测)
论文:https://arxiv.org/pdf/1906.06310v3.pdf
代码:https://github.com/mileyan/Pseudo_Lidar_V2
19. star:182|Learning to Explore using Active Neural SLAM(三维SLAM)
论文:https://arxiv.org/pdf/2004.05155v1.pdf
代码:https://github.com/devendrachaplot/Neural-SLAM
20. star:175|Mutual Mean-Teaching: Pseudo Label Refinery for Unsupervised Domain Adaptation on Person Re-identification(行人重识别/无监督)
论文:https://arxiv.org/pdf/2001.01526v2.pdf
代码:https://github.com/yxgeee/MMT
21. star:132|AtomNAS: Fine-Grained End-to-End Neural Architecture Search(神经网络架构搜索)
论文:https://arxiv.org/pdf/1912.09640v2.pdf
代码:https://github.com/meijieru/AtomNAS
22. star:128|Strategies for Pre-training Graph Neural Networks(神经网络训练)
论文:https://arxiv.org/pdf/1905.12265v3.pdf
代码:https://github.com/snap-stanford/pretrain-gnns/
23. star117|Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization(归一化)
论文:https://arxiv.org/pdf/2001.06838v2.pdf
代码:https://github.com/megvii-model/MABN
24. star:107|DropEdge: Towards Deep Graph Convolutional Networks on Node Classification
论文:https://arxiv.org/pdf/1907.10903v4.pdf
代码:https://github.com/DropEdge/DropEdge
25. star:107|Neural Arithmetic Units
论文:https://arxiv.org/pdf/2001.05016v1.pdf
代码:https://github.com/AndreasMadsen/stable-nalu
26. star:106|Semantically-Guided Representation Learning for Self-Supervised Monocular Depth(单目深度估计)
论文:https://arxiv.org/pdf/2002.12319v1.pdf
代码:https://github.com/TRI-ML/packnet-sfm
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27. star:100|Composition-based Multi-Relational Graph Convolutional Networks
论文:https://arxiv.org/pdf/1911.03082v2.pdf
代码:https://github.com/malllabiisc/CompGCN
28. star:93|Deformable Kernels: Adapting Effective Receptive Fields for Object Deformation(图像分割/目标检测)
论文:https://arxiv.org/pdf/1910.02940v2.pdf
代码:https://github.com/hangg7/deformable-kernels/
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29. star:80|NAS evaluation is frustratingly hard(神经网络架构搜索)
论文:https://arxiv.org/pdf/1912.12522v3.pdf
代码:https://github.com/antoyang/NAS-Benchmark
30. star:74|Understanding and Robustifying Differentiable Architecture Search(图像分类)
论文:https://arxiv.org/pdf/1909.09656v2.pdf
代码:https://github.com/automl/RobustDARTS
31. star:72|Fast Neural Network Adaptation via Parameter Remapping and Architecture Search(图像分类/目标检测/语义分割)
论文:https://arxiv.org/pdf/2001.02525v2.pdf
代码:https://github.com/JaminFong/FNA
32. star:72|Capsules with Inverted Dot-Product Attention Routing(图像分类)
论文:https://arxiv.org/pdf/2002.04764v2.pdf
代码:https://github.com/apple/ml-capsules-inverted-attention-routing
33. star:53|Deep Semi-Supervised Anomaly Detection(异常检测)
论文:https://arxiv.org/pdf/1906.02694v2.pdf
代码:https://arxiv.org/pdf/1906.02694v2.pdf
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34. star:51|Network Deconvolution(图像分类)
论文:https://arxiv.org/pdf/1905.11926v4.pdf
代码:https://github.com/deconvolutionpaper/deconvolution
35. star:49|Pitfalls of In-Domain Uncertainty Estimation and Ensembling in Deep Learning(图像分类)
论文:https://arxiv.org/pdf/2002.06470v1.pdf
代码:https://github.com/bayesgroup/pytorch-ensembles
36. star:36|A Neural Dirichlet Process Mixture Model for Task-Free Continual Learning(图像分类)
论文:https://arxiv.org/pdf/2001.00689v2.pdf
代码:https://github.com/soochan-lee/CN-DPM
37. star:33|Empirical Bayes Transductive Meta-Learning with Synthetic Gradients(小样本图像分类/元学习)
论文:https://openreview.net/pdf?id=Hkg-xgrYvH
代码:https://github.com/hushell/sib_meta_learn
38. star:32|Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings(知识图谱)
论文:https://arxiv.org/pdf/2002.05969v2.pdf
代码:https://github.com/hyren/query2box
39. star:27|Kaleidoscope: An Efficient, Learnable Representation For All Structured Linear Maps(图像分类)
论文:https://openreview.net/pdf?id=BkgrBgSYDS
代码:https://github.com/HazyResearch/learning-circuits
40. star:22|Fooling Detection Alone is Not Enough: Adversarial Attack against Multiple Object Tracking(多目标跟踪)
论文:https://openreview.net/pdf?id=rJl31TNYPr
代码:https://github.com/anonymousjack/hijacking