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}  
}

迁移学习 Transfer Learning

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

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

Weekly latest papers
- 20200702 [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
- 20200629 [ICML-20] Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
  - Source-free adaptation
  - 在adaptation过程中不访问source data
- 20200629 Transfer learning via L1 regulaziation
  - Using L1 regularizationg for transfer learning
- 20200629 [ICML-20] [Graph Optimal Transport for Cross-Domain Alignment])(https://arxiv.org/abs/2006.14744)
  - Graph OT for cross-domain alignment

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
Brief introduction and slides 简介与ppt资料
- Chinese introduction
- PPT (English)
- PPT (中文)
- 迁移学习中的领域自适应方法 Domain adaptation: PDF ｜ Video
- Transfer learning report by Mingsheng Long @ THU - 清华大学龙明盛老师的深度迁移学习报告：PPT(Samsung)、PPT(Google China)
Talk is cheap, show me the code 动手教程、代码、数据
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？
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2.Transfer Learning Areas and Papers (研究领域与相关论文)

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：
- 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 - 近期值得注意的理论分析文章：
- ICML-19 Bridging Theory and Algorithm for Domain Adaptation
- 最近几年在ICML、NIPS、COLT、ALT上出现了一些理论分析的文章，以domain adaptation为关键字可以搜索到。
理论研究方面，重点关注Alex Smola、Ben-David、Bernhard Schölkopf、Arthur Gretton等人的研究。
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.

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.

General transfer learning algorithms and applications:
- Qiang Yang：中文名杨强。香港科技大学计算机系讲座教授，迁移学习领域世界性专家。IEEE/ACM/AAAI/IAPR/AAAS fellow。[Google scholar]
- Sinno Jialin Pan：杨强的学生，香港科技大学博士，现任新加坡南洋理工大学助理教授。迁移学习领域代表性综述A survey on transfer learning的第一作者（Qiang Yang是二作）。[Google scholar]
Transfer learning algorithms:
- Wenyuan Dai：中文名戴文渊，上海交通大学硕士，现任第四范式人工智能创业公司CEO。迁移学习领域著名的牛人，在顶级会议上发表多篇高水平文章，每篇论文引用量巨大。[Google scholar]
- Mingsheng Long：中文名龙明盛，清华大学博士，现任清华大学助理教授、博士生导师。[Google scholar]
- Lixin Duan：中文名段立新，新加坡南洋理工大学博士，现就职于电子科技大学，教授。[Google scholar]
Transfer learning + computer vision
- Boqing Gong：南加州大学博士，现就职于腾讯AI Lab(西雅图)。曾任中佛罗里达大学助理教授。[Google scholar]
- Tatiana Tommasi：Researcher at the Italian Institute of Technology.
- Vinod K Kurmi[home page]: Researcher at the Indian Institute of Technology Kanpur(India)
Transfer learning + recommendation systems
- Weike Pan：中文名潘微科，杨强的学生，现任深圳大学副教授，香港科技大学博士毕业。主要做迁移学习在推荐系统方面的一些工作。 [Google Scholar]
- Fuzhen Zhuang：中文名庄福振，中科院计算所博士，现任中科院计算所副研究员。[Google scholar]
Online transfer learning:
- Qingyao Wu：中文名吴庆耀，现任华南理工大学副教授。主要做在线迁移学习、异构迁移学习方面的研究。[Google scholar]
Theory:
- Tongliang Liu：中文名刘同亮，现任悉尼大学助理教授。主要做迁移学习的一些理论方面的工作。[Google scholar]

6.Transfer Learning Thesis (硕博士论文)

Here are some popular thesis on transfer learning.

硕博士论文可以让我们很快地对迁移学习的相关领域做一些了解，同时，也能很快地了解概括相关研究者的工作。其中，比较有名的有

2016 Baochen Sun的Correlation Alignment for Domain Adaptation
2015 南加州大学的Boqing Gong的Kernel Methods for Unsupervised Domain Adaptation
2014 清华大学龙明盛的迁移学习问题与方法研究
2014 中科院计算所赵中堂的自适应行为识别中的迁移学习方法研究
2012 杨强的学生Hao Hu的Learning based Activity Recognition
2012 杨强的学生Wencheng Zheng的Learning with Limited Data in Sensor-based Human Behavior Prediction
2010 杨强的学生Sinno Jialin Pan的Feature-based Transfer Learning and Its Applications
2009 上海交通大学戴文渊的基于实例和特征的迁移学习算法研究

其他的文章，请见完整版。

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)

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:
- DLKT: Deep Learning for Knowledge Transfer @ ICDM 2020
Related projects:
- Salad: A semi-supervised domain adaptation library
- Dassl: A PyTorch toolbox for domain adaptation and semi-supervised learning
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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！