Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Papers • Tutorials • Research areas • Theory • Survey • Code • Dataset & benchmark
Thesis • Scholars • Contests • Journal/conference • Applications • Others • Contributing
**Widely used by top conferences and journals:** - Conferences: [[CVPR'22](https://openaccess.thecvf.com/content/CVPR2022W/FaDE-TCV/html/Zhang_Segmenting_Across_Places_The_Need_for_Fair_Transfer_Learning_With_CVPRW_2022_paper.html)] [[NeurIPS'21](https://proceedings.neurips.cc/paper/2021/file/731b03008e834f92a03085ef47061c4a-Paper.pdf)] [[IJCAI'21](https://arxiv.org/abs/2103.03097)] [[ESEC/FSE'20](https://dl.acm.org/doi/abs/10.1145/3368089.3409696)] [[IJCNN'20](https://ieeexplore.ieee.org/abstract/document/9207556)] [[ACMMM'18](https://dl.acm.org/doi/abs/10.1145/3240508.3240512)] [[ICME'19](https://ieeexplore.ieee.org/abstract/document/8784776/)] - Journals: [[IEEE TKDE](https://ieeexplore.ieee.org/abstract/document/9782500/)] [[ACM TIST](https://dl.acm.org/doi/abs/10.1145/3360309)] [[Information sciences](https://www.sciencedirect.com/science/article/pii/S0020025520308458)] [[Neurocomputing](https://www.sciencedirect.com/science/article/pii/S0925231221007025)] [[IEEE Transactions on Cognitive and Developmental Systems](https://ieeexplore.ieee.org/abstract/document/9659817)] ``` @Misc{transferlearning.xyz, howpublished = {\url{http://transferlearning.xyz}}, title = {Everything about Transfer Learning and Domain Adapation}, author = {Wang, Jindong and others} } ``` [](https://opensource.org/licenses/MIT) [](https://github.com/996icu/996.ICU/blob/master/LICENSE) [](https://996.icu) Related Codes: - Large language model evaluation: [[llm-eval](https://llm-eval.github.io/)] - Large language model enhancement: [[llm-enhance](https://llm-enhance.github.io/)] - Robust machine learning: [[robustlearn: robust machine learning](https://github.com/microsoft/robustlearn)] - Semi-supervised learning: [[USB: unified semi-supervised learning benchmark](https://github.com/microsoft/Semi-supervised-learning)] | [[TorchSSL: a unified SSL library](https://github.com/TorchSSL/TorchSSL)] - LLM benchmark: [[PromptBench: adversarial robustness of prompts of LLMs](https://github.com/microsoft/promptbench)] - Federated learning: [[PersonalizedFL: library for personalized federated learning](https://github.com/microsoft/PersonalizedFL)] - Activity recognition and machine learning [[Activity recognition](https://github.com/jindongwang/activityrecognition)]｜[[Machine learning](https://github.com/jindongwang/MachineLearning)] - - - **NOTE:** You can directly open the code in [Gihub Codespaces](https://docs.github.com/en/codespaces/getting-started/quickstart#introduction) on the web to run them without downloading! Also, try [github.dev](https://github.dev/jindongwang/transferlearning). ## 0.Papers (论文) [Awesome transfer learning papers (迁移学习文章汇总)](https://github.com/jindongwang/transferlearning/tree/master/doc/awesome_paper.md) - [Paperweekly](http://www.paperweekly.site/collections/231/papers): A website to recommend and read paper notes **Latest papers**: - By topic: [doc/awesome_papers.md](/doc/awesome_paper.md) - By date: [doc/awesome_paper_date.md](/doc/awesome_paper_date.md) *Updated at 2024-02-18:* - Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification [[arxiv](https://arxiv.org/abs/2502.10694)] - Unsupervised domain adaptaiton for image classification - Semantics-aware Test-time Adaptation for 3D Human Pose Estimation [[arxiv](https://arxiv.org/abs/2502.10724)] - Test-time adaptation for3D human pose estimation - Transfer Learning of CATE with Kernel Ridge Regression [[arxiv](https://arxiv.org/abs/2502.11331)] - Transfer learning with kernel ridge regression - Why Domain Generalization Fail? A View of Necessity and Sufficiency [[arxiv](https://arxiv.org/abs/2502.10716)] - 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](https://arxiv.org/abs/2502.06272)] - 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。 - Books 书籍 - **Introduction to Transfer Learning: Algorithms and Practice** [[Buy or read](https://link.springer.com/book/9789811975837)] - **《迁移学习》（杨强）** [[Buy](https://item.jd.com/12930984.html)] [[English version](https://www.cambridge.org/core/books/transfer-learning/CCFFAFE3CDBC245047F1DEC71D9EF3C7)] - **《迁移学习导论》(王晋东、陈益强著)** [[Homepage](http://jd92.wang/tlbook)] [[Buy](https://item.jd.com/13272157.html)] - Blogs 博客 - [Zhihu blogs - 知乎专栏《小王爱迁移》系列文章](https://zhuanlan.zhihu.com/p/130244395) - Video tutorials 视频教程 - Transfer learning 迁移学习: - [Recent advance of transfer learning - 2022年最新迁移学习发展现状探讨](https://www.bilibili.com/video/BV1nY411E7Uc/) - [Definitions of transfer learning area - 迁移学习领域名词解释](https://www.bilibili.com/video/BV1fu411o7BW) [[Article](https://zhuanlan.zhihu.com/p/428097044)] - [Transfer learning by Hung-yi Lee @ NTU - 台湾大学李宏毅的视频讲解(中文视频)](https://www.youtube.com/watch?v=qD6iD4TFsdQ) - Domain generalization 领域泛化： - [IJCAI-ECAI'22 tutorial on domain generalization - 领域泛化tutorial](https://dgresearch.github.io/) - [Domain generalization - 迁移学习新兴研究方向领域泛化](https://www.bilibili.com/video/BV1ro4y1S7dd/) - Domain adaptation 领域自适应： - [Domain adaptation - 迁移学习中的领域自适应方法(中文)](https://www.bilibili.com/video/BV1T7411R75a/) - Brief introduction and slides 简介与ppt资料 - [Recent advance of transfer learning](https://jd92.wang/assets/files/l16_aitime.pdf) - [Domain generalization survey](http://jd92.wang/assets/files/DGSurvey-ppt.pdf) - [Brief introduction in Chinese](https://github.com/jindongwang/transferlearning/blob/master/doc/%E8%BF%81%E7%A7%BB%E5%AD%A6%E4%B9%A0%E7%AE%80%E4%BB%8B.md) - [PPT (English)](http://jd92.wang/assets/files/l03_transferlearning.pdf) | [PPT (中文)](http://jd92.wang/assets/files/l08_tl_zh.pdf) - 迁移学习中的领域自适应方法 Domain adaptation: [PDF](http://jd92.wang/assets/files/l12_da.pdf) ｜ [Video on Bilibili](https://www.bilibili.com/video/BV1T7411R75a/) | [Video on Youtube](https://www.youtube.com/watch?v=RbIsHNtluwQ&t=22s) - Tutorial on transfer learning by Qiang Yang: [IJCAI'13](http://ijcai13.org/files/tutorial_slides/td2.pdf) | [2016 version](http://kddchina.org/file/IntroTL2016.pdf) - Talk is cheap, show me the code 动手教程、代码、数据 - [Pytorch tutorial on transfer learning](https://pytorch.org/tutorials/beginner/transfer_learning_tutorial.html) - [Pytorch finetune](https://github.com/jindongwang/transferlearning/tree/master/code/AlexNet_ResNet) - [DeepDA: a unified deep domain adaptation toolbox](https://github.com/jindongwang/transferlearning/tree/master/code/DeepDA) - [DeepDG: a unified deep domain generalization toolbox](https://github.com/jindongwang/transferlearning/tree/master/code/DeepDG) - [更多 More...](https://github.com/jindongwang/transferlearning/tree/master/code) - [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](https://github.com/jindongwang/transferlearning/blob/master/doc/scholar_TL.md) - [Negative transfer - 负迁移](https://www.zhihu.com/question/66492194/answer/242870418) - - - ## 2.Transfer Learning Areas and Papers (研究领域与相关论文) - [Survey](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#survey) - [Theory](#theory) - [Per-training/Finetuning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#per-trainingfinetuning) - [Knowledge distillation](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#knowledge-distillation) - [Traditional domain adaptation](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#traditional-domain-adaptation) - [Deep domain adaptation](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#deep-domain-adaptation) - [Domain generalization](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#domain-generalization) - [Source-free domain adaptation](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#source-free-domain-adaptation) - [Multi-source domain adaptation](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#multi-source-domain-adaptation) - [Heterogeneous transfer learning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#heterogeneous-transfer-learning) - [Online transfer learning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#online-transfer-learning) - [Zero-shot / few-shot learning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#zero-shot--few-shot-learning) - [Multi-task learning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#multi-task-learning) - [Transfer reinforcement learning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#transfer-reinforcement-learning) - [Transfer metric learning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#transfer-metric-learning) - [Federated transfer learning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#federated-transfer-learning) - [Lifelong transfer learning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#lifelong-transfer-learning) - [Safe transfer learning](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#safe-transfer-learning) - [Transfer learning applications](https://github.com/jindongwang/transferlearning/blob/master/doc/awesome_paper.md#transfer-learning-applications) - - - ## 3.Theory and Survey (理论与综述) Here are some articles on transfer learning theory and survey. **Survey (综述文章)：** - 2023 Source-Free Unsupervised Domain Adaptation: A Survey [[arxiv](http://arxiv.org/abs/2301.00265)] - 2022 [Transfer Learning for Future Wireless Networks: A Comprehensive Survey](https://arxiv.org/abs/2102.07572) - 2022 [A Review of Deep Transfer Learning and Recent Advancements](https://arxiv.org/abs/2201.09679) - 2022 [Transferability in Deep Learning: A Survey](https://paperswithcode.com/paper/transferability-in-deep-learning-a-survey), from Mingsheng Long in THU. - 2021 Domain generalization: IJCAI-21 [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://arxiv.org/abs/2103.03097) | [知乎文章](https://zhuanlan.zhihu.com/p/354740610) | [微信公众号](https://mp.weixin.qq.com/s/DsoVDYqLB1N7gj9X5UnYqw) - First survey on domain generalization - 第一篇对Domain generalization (领域泛化)的综述 - 2021 Vision-based activity recognition: [A Survey of Vision-Based Transfer Learning in Human Activity Recognition](https://www.mdpi.com/2079-9292/10/19/2412) - 2021 I