Papers with Code 中收集了各种机器学习的内容:论文,代码,结果,方便发现和比较。通过这些数据,我们可以了解ML社区中,今年哪些东西最有意思。下面我们总结了2020年最热门的带代码的论文、代码库和benchmark。
2020顶流论文
Tan等人的EfficientDet是2020年在Papers with Code上被访问最多的论文。
EfficientDet: Scalable and Efficient Object Detection — Tan et al https://paperswithcode.com/paper/efficientdet-scalable-and-efficient-object
Fixing the train-test resolution discrepancy — Touvron et al https://paperswithcode.com/paper/fixing-the-train-test-resolution-discrepancy-2
ResNeSt: Split-Attention Networks — Zhang et al https://paperswithcode.com/paper/resnest-split-attention-networks
Big Transfer (BiT) — Kolesnikov et al https://paperswithcode.com/paper/large-scale-learning-of-general-visual
Object-Contextual Representations for Semantic Segmentation — Yuan et al https://paperswithcode.com/paper/object-contextual-representations-for
Self-training with Noisy Student improves ImageNet classification — Xie et al https://paperswithcode.com/paper/self-training-with-noisy-student-improves
YOLOv4: Optimal Speed and Accuracy of Object Detection — Bochkovskiy et al https://paperswithcode.com/paper/yolov4-optimal-speed-and-accuracy-of-object
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale — Dosovitskiy et al https://paperswithcode.com/paper/an-image-is-worth-16x16-words-transformers-1
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer — Raffel et al https://paperswithcode.com/paper/exploring-the-limits-of-transfer-learning
Hierarchical Multi-Scale Attention for Semantic Segmentation — Tao et al https://paperswithcode.com/paper/hierarchical-multi-scale-attention-for
2020顶流代码库
Transformers是2020年在Papers with Code上被访问最多的代码库
Transformers — Hugging Face — https://github.com/huggingface/transformers
PyTorch Image Models — Ross Wightman — https://github.com/rwightman/pytorch-image-models