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Everything about Transfer Learning and Domain Adaptation--迁移学习

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迁移学习 Transfer Learning

Awesome MIT License LICENSE


Everything about Transfer Learning.

PapersTutorialsResearch areasTheorySurveyCodeDataset & benchmark


howpublished = {\url{}},   
title = {Everything about Transfer Learning and Domain Adapation},  
author = {Wang, Jindong and others}  

More related repos:Activity recognitionMachine learning

NOTE: You can directly open the code in Gihub Codespaces on the web to run them without downloading! Also, try

0.Papers (论文)

Awesome transfer learning papers (迁移学习文章汇总)

  • Paperweekly: A good website to recommend and read paper notes (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)

Latest papers: (All papers are also put in doc/

Latest papers (2021-10-21)
Latest papers (2021-10-19)
Latest papers (2021-10-14)
Latest papers (2021-10-11)
Latest papers (2021-09)
Latest papers (2021-08)
Latest papers (2021-07)

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。

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):

Theory (理论文章):

4.Code (代码)

Unified codebases for: - Deep domain adaptation - Deep domain generalization

More: see HERE and 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.


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.


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

Applications (迁移学习应用)

See HERE for transfer learning applications.


Other Resources (其他资源)

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.

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