自训练：Self-training with Noisy Student improves ImageNet classification 论文：https://arxiv.org/abs/1911.04252 代码：https://github.com/google-research/noisystudent 解读：https://zhuanlan.zhihu.com/p/164597142、 https://blog.csdn.net/qq_39426225/article/details/105571340
SimCLRv1：A Simple Framework for Contrastive Learning of Visual Representations 论文：https://arxiv.org/abs/2002.05709 代码：https://github.com/google-research/simclr、https://github.com/Spijkervet/SimCLR、https://github.com/sthalles/SimCLR 解读：https://blog.csdn.net/u011984148/article/details/106233313/、 https://blog.csdn.net/m0_37929824/article/details/104440694 SimCLRv2：Big Self-Supervised Models are Strong Semi-Supervised Learners 论文：https://arxiv.org/abs/2006.10029 代码：https://github.com/google-research/simclr 解读：https://blog.csdn.net/qq_33204646/article/details/107667230 MoCov1：Momentum Contrast for Unsupervised Visual Representation Learning 论文：https://arxiv.org/pdf/1911.05722.pdf 代码：https://github.com/facebookresearch/moco 解读：https://www.zhihu.com/question/355779873、 https://blog.csdn.net/FatMigo/article/details/103211622 MoCov2：Improved Baselines with Momentum Contrastive Learning 论文：https://arxiv.org/pdf/2003.04297.pdf 代码：https://github.com/facebookresearch/moco 解读：https://zhuanlan.zhihu.com/p/114048224
Meta Pseudo Labels 论文：https://arxiv.org/pdf/2003.10580.pdf 代码：https://github.com/google-research/google-research/tree/master/meta_pseudo_labels 解读：https://blog.csdn.net/BAAIBeijing/article/details/112856194
汇总：https://github.com/dkozlov/awesome-knowledge-distillation 综述：https://zhuanlan.zhihu.com/p/93287223?from_voters_page=true
PaddleClas-SSLD 文档：https://paddleclas.readthedocs.io/zh_CN/latest/advanced_tutorials/distillation/index.html https://github.com/PaddlePaddle/PaddleClas/blob/master/docs/zh_CN/advanced_tutorials/distillation/distillation.md 代码：https://github.com/PaddlePaddle/PaddleClas 方法梳理：https://blog.csdn.net/u012526003/article/details/106160464 MEAL: Multi-Model Ensemble via Adversarial Learning 论文：https://arxiv.org/abs/1812.02425 代码：https://github.com/AaronHeee/MEAL#imagenet-model 解读：https://www.cnblogs.com/White-xzx/p/10313982.html
MEAL V2: Boosting Vanilla ResNet-50 to 80%+ Top-1 Accuracy on ImageNet without Tricks 论文：https://arxiv.org/abs/2009.08453 代码：https://github.com/szq0214/MEAL-V2 解读：https://cloud.tencent.com/developer/article/1703632
