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}
}
NOTE: You can directly open the code in Gihub Codespaces on the web to run them without downloading! See this figure:
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
20210329 Adversarial Branch Architecture Search for Unsupervised Domain Adaptation
20210329 ICLR-21 Tent: Fully Test-Time Adaptation by Entropy Minimization
20210319 Learning Invariant Representations across Domains and Tasks
20210319 Generalizing to Unseen Domains: A Survey on Domain Generalization | 知乎文章 | 微信公众号
20210319 Cross-domain Activity Recognition via Substructural Optimal Transport | 知乎文章 | 微信公众号
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
Video tutorials 视频教程
Brief introduction and slides 简介与ppt资料
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
Related articles by research areas:
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。
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:
Survey on applications - 应用导向的综述:
Theory (理论文章):
Early transfer learning theory papers - 早期迁移学习的理论分析文章:
Latest theory papers
ICML-20 Few-shot domain adaptation by causal mechanism transfer
CVPR-19 Characterizing and Avoiding Negative Transfer
ICML-20 On Learning Language-Invariant Representations for Universal Machine Translation
MMD (Maximum mean discrepancy):
请见这里 | Please see HERE for some popular transfer learning codes.
See HERE for an instant run using Google's Colab.
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.
Here are some popular thesis on transfer learning.
这里, 提取码:txyz。
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。
See HERE for transfer learning applications.
迁移学习应用请见这里。
Call for papers:
Related projects:
If you are interested in contributing, please refer to HERE for instructions in contribution.
[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!
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