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Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习

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Transfer Leanring

Everything about Transfer Learning. 迁移学习.

PapersTutorialsResearch areasTheorySurveyCodeDataset & benchmark

ThesisScholarsContestsJournal/conferenceApplicationsOthersContributing

Widely used by top conferences and journals:

@Misc{transferlearning.xyz,
howpublished = {\url{http://transferlearning.xyz}},   
title = {Everything about Transfer Learning and Domain Adapation},  
author = {Wang, Jindong and others}  
}  

Awesome MIT License LICENSE 996.icu

Related Codes:


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:

Updated at 2024-10-21:

  • Transfer Learning on Multi-Dimensional Data: A Novel Approach to Neural Network-Based Surrogate Modeling [arxiv]

    • Transfer learning on multi-dimensioal data
  • TransAgent: Transfer Vision-Language Foundation Models with Heterogeneous Agent Collaboration [arxiv]

    • Transfer vision-language models for collaboration
  • Test-time adaptation for image compression with distribution regularization [arxiv]

    • Test-time adaptation for image compression with distribution regularization
  • WeatherDG: LLM-assisted Procedural Weather Generation for Domain-Generalized Semantic Segmentation [arxiv]

    • Weather domain generalization

Updated at 2024-10-16:

  • Can In-context Learning Really Generalize to Out-of-distribution Tasks? [arxiv]

    • Can in-context learning generalize to OOD tasks?
  • Domain-Conditioned Transformer for Fully Test-time Adaptation [arxiv]

    • Fully test-tim adaptation with domain-conditioned transformer
  • Safety-Aware Fine-Tuning of Large Language Models [arxiv]

    • Fine-tuning with safety in LLMs
    • Deep Transfer Learning: Model Framework and Error Analysis [arxiv]
    • Deep transfer learning framework
  • Cross-Domain Distribution Alignment for Segmentation of Private Unannotated 3D Medical Images [arxiv]

    • Cross-domain adaptation of private unannotated 3D medical images
  • Stratified Domain Adaptation: A Progressive Self-Training Approach for Scene Text Recognition [arxiv]

    • Stratified domain adaptation

Updated at 2024-10-12:

  • Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs [arxiv]

    • Active fine-tuning of LLMs
  • LLM Embeddings Improve Test-time Adaptation to Tabular Y|X shifts [arxiv]

    • Test-time adaptation via LLMs
  • AHA: Human-Assisted Out-of-Distribution Generalization and Detection [arxiv]

    • Human-assisted OOD generalization and detection

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:

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.

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.


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.

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)


Journals and Conferences

See here for a full list of related journals and conferences.


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)


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.