迁移学习 Transfer Learning

Everything about Transfer Learning (Probably the most complete repository?). Your contribution is highly valued! If you find this repo helpful, please cite it as follows:
关于迁移学习的所有资料，包括：介绍、综述文章、最新文章、代表工作及其代码、常用数据集、硕博士论文、比赛等等。(可能是目前最全的迁移学习资料库？) 欢迎一起贡献！ 如果认为本仓库有用，请在你的论文和其他出版物中进行引用！

@Misc{transferlearning.xyz,
howpublished = {\url{http://transferlearning.xyz}},   
title = {Everything about Transfer Learning and Domain Adapation},  
author = {Wang, Jindong and others}  
}

| Contents | | —- | | 0.Latest Publications (最新论文) | 1.Introduction and Tutorials (简介与教程) | | 2.Transfer Learning Areas and Papers (研究领域与相关论文) | 3.Theory and Survey (理论与综述) | | 4.Code (代码) | 5.Transfer Learning Scholars (著名学者) | | 6.Transfer Learning Thesis (硕博士论文) | 7.Datasets and Benchmarks (数据集与评测结果) | | 8.Transfer Learning Challenges (迁移学习比赛) | Applications (迁移学习应用) | | Other Resources (其他资源) | Contributing (欢迎参与贡献) |

关于机器学习和行为识别的资料，请参考：行为识别｜机器学习

NOTE: You can directly open the code in Gihub Codespaces on the web to run them without downloading! See this figure:

0.Latest Publications (最新论文)

A good website to see the latest arXiv preprints by search: Transfer learning, Domain adaptation
一个很好的网站，可以直接看到最新的arXiv文章: Transfer learning, Domain adaptation
迁移学习文章汇总 Awesome transfer learning papers

• Latest papers

• 20210426 Distill on the Go: Online knowledge distillation in self-supervised learning

• Online knowledge distillation in self-supervised learning
• 自监督学习中的在线知识蒸馏

• 20210426 Few-shot Continual Learning: a Brain-inspired Approach
• Few-shot continual learning
• 小样本持续学习

• 20210420 arXiv Cross-domain Speech Recognition with Unsupervised Character-level Distribution Matching
• Domain adaptation for speech recognition
• 用domain adaptation进行跨领域的语音识别

• 20210420 arXiv Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey
• A survey on domain adaptation for machine translation
• 关于用领域自适应进行神经机器翻译的综述

• 20210420 arXiv On Universal Black-Box Domain Adaptation
• Universal black-box domain adaptation
• 黑盒情况下的universal domain adaptation

更多 More…

1.Introduction and Tutorials (简介与教程)

Want to quickly learn transfer learning？想尽快入门迁移学习？看下面的教程。

• The first transfer learning tutorial 入门教程

• 《迁移学习简明手册》Transfer Learning Tutorial Read online, PDF

• Zhihu blogs - 知乎专栏《小王爱迁移》系列文章

• Video tutorials 视频教程

• Domain adaptation - 迁移学习中的领域自适应方法(中文)

• Transfer learning by Hung-yi Lee @ NTU - 台湾大学李宏毅的视频讲解(中文视频)
• Chelsea finn’s Stanford CS330 class on multi-task and meta-learning - 2020斯坦福大学多任务与元学习教程CS330

• Brief introduction and slides 简介与ppt资料

• Brief introduction in Chinese

• PPT (English) | PPT (中文)
• 迁移学习中的领域自适应方法 Domain adaptation: PDF ｜ Video on Bilibili | Video on Youtube
• Tutorial on transfer learning by Qiang Yang: IJCAI’13 | 2016 version

• Talk is cheap, show me the code 动手教程、代码、数据

• Pytorch官方迁移学习示意代码

• Pytorch的finetune Fine-tune based on Alexnet and Resnet
• 用Pytorch进行深度特征提取
• 更多 More…

• Transfer Learning Scholars and Labs - 迁移学习领域的著名学者、代表工作及实验室介绍

• Negative transfer - 负迁移

2.Transfer Learning Areas and Papers (研究领域与相关论文)

Related articles by research areas:

• General Transfer Learning (普通迁移学习)
• Theory (理论)
• Others (其他)

• Domain Adaptation (领域自适应)
• Traditional Methods (传统迁移方法)
• Deep / Adversarial Methods (深度/对抗迁移方法)

• Domain Generalization
• Multi-source Transfer Learning (多源迁移学习)
• Heterogeneous Transfer Learning (异构迁移学习)
• Online Transfer Learning (在线迁移学习)
• Zero-shot / Few-shot Learning
• Deep Transfer Learning (深度迁移学习)
• Non-Adversarial Transfer Learning (非对抗深度迁移)
• Deep Adversarial Transfer Learning (对抗迁移学习)

• Multi-task Learning (多任务学习)
• Transfer Reinforcement Learning (强化迁移学习)
• Transfer Metric Learning (迁移度量学习)
• Transitive Transfer Learning (传递迁移学习)
• Lifelong Learning (终身迁移学习)
• Negative Transfer (负迁移)
• Transfer Learning Applications (应用)

Paperweekly: 一个推荐、分享论文的网站比较好，上面会持续整理相关的文章并分享阅读笔记。

3.Theory and Survey (理论与综述)

Here are some articles on transfer learning theory and survey.
Survey (综述文章)：

• The most influential survey on transfer learning （最权威和经典的综述）: A survey on transfer learning.

• Latest survey - 较新的综述：

• 2021 Domain generalization: IJCAI-21 Generalizing to Unseen Domains: A Survey on Domain Generalization | 知乎文章 | 微信公众号

• First survey on domain generalization
• 第一篇对Domain generalization (领域泛化)的综述

• 2020 迁移学习最新survey，来自中科院计算所庄福振团队，发表在Proceedings of the IEEE: A Comprehensive Survey on Transfer Learning
• 2020 负迁移的综述：Overcoming Negative Transfer: A Survey
• 2020 知识蒸馏的综述: Knowledge Distillation: A Survey
• 用transfer learning进行sentiment classification的综述：A Survey of Sentiment Analysis Based on Transfer Learning
• 2019 一篇新survey：Transfer Adaptation Learning: A Decade Survey
• 2018 一篇迁移度量学习的综述: Transfer Metric Learning: Algorithms, Applications and Outlooks
• 2018 一篇最近的非对称情况下的异构迁移学习综述：Asymmetric Heterogeneous Transfer Learning: A Survey
• 2018 Neural style transfer的一个survey：Neural Style Transfer: A Review
• 2018 深度domain adaptation的一个综述：Deep Visual Domain Adaptation: A Survey
• 2017 多任务学习的综述，来自香港科技大学杨强团队：A survey on multi-task learning
• 2017 异构迁移学习的综述：A survey on heterogeneous transfer learning
• 2017 跨领域数据识别的综述：Cross-dataset recognition: a survey
• 2016 A survey of transfer learning。其中交代了一些比较经典的如同构、异构等学习方法代表性文章。
• 2015 中文综述：迁移学习研究进展

• Survey on applications - 应用导向的综述：

• 视觉domain adaptation综述：Visual Domain Adaptation: A Survey of Recent Advances

• 迁移学习应用于行为识别综述：Transfer Learning for Activity Recognition: A Survey
• 迁移学习与增强学习：Transfer Learning for Reinforcement Learning Domains: A Survey
• 多个源域进行迁移的综述：A Survey of Multi-source Domain Adaptation。

Theory （理论文章）:

• Early transfer learning theory papers - 早期迁移学习的理论分析文章：

• NIPS-06 Analysis of Representations for Domain Adaptation

• ML-10 A Theory of Learning from Different Domains
• NIPS-08 Learning Bounds for Domain Adaptation
• COLT-09 Domain adaptation: Learning bounds and algorithms

• Latest theory papers

• ICML-20 Few-shot domain adaptation by causal mechanism transfer

• The first work on causal transfer learning

• 日本理论组大佬Sugiyama的工作，causal transfer learning

• CVPR-19 Characterizing and Avoiding Negative Transfer

• Characterizing and avoid negative transfer

• 形式化并提出如何避免负迁移

• ICML-20 On Learning Language-Invariant Representations for Universal Machine Translation

• Theory for universal machine translation

• 对统一机器翻译模型进行了理论论证

• MMD (Maximum mean discrepancy):

• MMD的提出：A Hilbert Space Embedding for Distributions 以及 A Kernel Two-Sample Test

• 多核MMD(MK-MMD)：Optimal kernel choice for large-scale two-sample tests
• MMD及多核MMD代码：Matlab | Python

4.Code (代码)

请见这里 | Please see HERE for some popular transfer learning codes.
See HERE for an instant run using Google’s Colab.

5.Transfer Learning Scholars (著名学者)

Here are some transfer learning scholars and labs.
全部列表以及代表工作性见这里
Please note that this list is far not complete. A full list can be seen in here. Transfer learning is an active field. If you are aware of some scholars, please add them here.

6.Transfer Learning Thesis (硕博士论文)

Here are some popular thesis on transfer learning.
这里, 提取码：txyz。

7.Datasets and Benchmarks (数据集与评测结果)

Please see HERE for the popular transfer learning datasets and benchmark results.
这里整理了常用的公开数据集和一些已发表的文章在这些数据集上的实验结果。

8.Transfer Learning Challenges (迁移学习比赛)

• Visual Domain Adaptation Challenge (VisDA)

Applications (迁移学习应用)

See HERE for transfer learning applications.
迁移学习应用请见这里。

Other Resources (其他资源)

• Call for papers:

• Advances in Transfer Learning: Theory, Algorithms, and Applications, DDL: October 2021

• Related projects:

• Salad: A semi-supervised domain adaptation library

• Dassl: A PyTorch toolbox for domain adaptation and semi-supervised learning

Contributing (欢迎参与贡献)

If you are interested in contributing, please refer to HERE for instructions in contribution.

Copyright notice

[Notes]This Github repo can be used by following the corresponding licenses. I want to emphasis that it may contain some PDFs or thesis, which were downloaded by me and can only be used for academic purposes. The copyrights of these materials are owned by corresponding publishers or organizations. All this are for better adademic research. If any of the authors or publishers have concerns, please contact me to delete or replace them.

[文章版权声明]这个仓库可以遵守相关的开源协议进行使用。这个仓库中包含有很多研究者的论文、硕博士论文等，都来源于在网上的下载，仅作为学术研究使用。我对其中一些文章都写了自己的浅见，希望能很好地帮助理解。这些文章的版权属于相应的出版社。如果作者或出版社有异议，请联系我进行删除。一切都是为了更好地学术！