Transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Install / Use
/learn @jindongwang/TransferlearningREADME
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<h1 align="center"> <br> <img src="png/logo.jpg" alt="Transfer Leanring" width="500"> </h1> <h4 align="center">Everything about Transfer Learning. 迁移学习.</h4> <p align="center"> <strong><a href="#0papers-论文">Papers</a></strong> • <strong><a href="#1introduction-and-tutorials-简介与教程">Tutorials</a></strong> • <a href="#2transfer-learning-areas-and-papers-研究领域与相关论文">Research areas</a> • <a href="#3theory-and-survey-理论与综述">Theory</a> • <a href="#3theory-and-survey-理论与综述">Survey</a> • <strong><a href="https://github.com/jindongwang/transferlearning/tree/master/code">Code</a></strong> • <strong><a href="#7datasets-and-benchmarks-数据集与评测结果">Dataset & benchmark</a></strong> </p> <p align="center"> <a href="#6transfer-learning-thesis-硕博士论文">Thesis</a> • <a href="#5transfer-learning-scholars-著名学者">Scholars</a> • <a href="#8transfer-learning-challenges-迁移学习比赛">Contests</a> • <a href="#journals-and-conferences">Journal/conference</a> • <a href="#applications-迁移学习应用">Applications</a> • <a href="#other-resources-其他资源">Others</a> • <a href="#contributing-欢迎参与贡献">Contributing</a> </p>Widely used by top conferences and journals:
- Conferences: [CVPR'22] [NeurIPS'21] [IJCAI'21] [ESEC/FSE'20] [IJCNN'20] [ACMMM'18] [ICME'19]
- Journals: [IEEE TKDE] [ACM TIST] [Information sciences] [Neurocomputing] [IEEE Transactions on Cognitive and Developmental Systems]
@Misc{transferlearning.xyz,
howpublished = {\url{http://transferlearning.xyz}},
title = {Everything about Transfer Learning and Domain Adapation},
author = {Wang, Jindong and others}
}
Related Codes:
- Large language model evaluation: [llm-eval]
- Large language model enhancement: [llm-enhance]
- Robust machine learning: [robustlearn: robust machine learning]
- Semi-supervised learning: [USB: unified semi-supervised learning benchmark] | [TorchSSL: a unified SSL library]
- LLM benchmark: [PromptBench: adversarial robustness of prompts of LLMs]
- Federated learning: [PersonalizedFL: library for personalized federated learning]
- Activity recognition and machine learning [Activity recognition]｜[Machine learning]
NOTE: You can directly open the code in Gihub Codespaces on the web to run them without downloading! Also, try github.dev.
0.Papers (论文)
Awesome transfer learning papers (迁移学习文章汇总)
- Paperweekly: A website to recommend and read paper notes
Latest papers:
- By topic: doc/awesome_papers.md
- By date: doc/awesome_paper_date.md
Updated at 2024-02-18:
-
Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification [arxiv]
- Unsupervised domain adaptaiton for image classification
-
Semantics-aware Test-time Adaptation for 3D Human Pose Estimation [arxiv]
- Test-time adaptation for3D human pose estimation
-
Transfer Learning of CATE with Kernel Ridge Regression [arxiv]
- Transfer learning with kernel ridge regression
-
Why Domain Generalization Fail? A View of Necessity and Sufficiency [arxiv]
- Analyze why domain generalization fail from the view of necessity and sufficiency
Updated at 2024-02-11:
- Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation [arxiv]
- Global awareness for enhanced domain adaptation
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。
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Books 书籍
- Introduction to Transfer Learning: Algorithms and Practice [Buy or read]
- 《迁移学习》（杨强） [Buy] [English version]
- 《迁移学习导论》(王晋东、陈益强著) [Homepage] [Buy]
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Blogs 博客
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Video tutorials 视频教程
- Transfer learning 迁移学习:
- Domain generalization 领域泛化：
- Domain adaptation 领域自适应：
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Brief introduction and slides 简介与ppt资料
- Recent advance of transfer learning
- Domain generalization survey
- Brief introduction in Chinese
- 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
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Talk is cheap, show me the code 动手教程、代码、数据
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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
