最高一万星!GitHub 标星最多的 40 篇 ICLR2020 计算机视觉论文合集,附打包下载
source link: https://bbs.cvmart.net/articles/2037
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
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
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
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
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
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
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
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
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
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
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/
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
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
链接:https://pan.baidu.com/s/1bM1ZAiX5FOY_65gBLAkoow
密码:提示:此内容登录后可查看
参考:https://paperswithcode.com/conference/iclr-2020-1/official
本文为极市平台整理报道,转载请联系本公众号获得授权。
微信公众号: 极市平台(ID: extrememart )
每天推送最新CV干货
Recommend
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK