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DEEMOALICE / transferlearning

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

Home Page:http://transferlearning.xyz/

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Transfer Leanring

Everything about Transfer Learning. 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:

@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 2023-04-18:

  • CVPR'23 Zero-shot Generative Model Adaptation via Image-specific Prompt Learning [arxiv]

    • Zero-shot generative model adaptation via image-specific prompt learning 零样本的生成模型adaptation

  • Source-free Domain Adaptation Requires Penalized Diversity [arxiv]

    • Source-free DA requires penalized diversity

  • Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation [arxiv]

    • Domain generalization for medical segmentation 用domain generalization进行医学分割

  • CVPR'23 Meta-causal Learning for Single Domain Generalization [arxiv]

    • Meta-causal learning for domain generalization

  • Domain Generalization In Robust Invariant Representation [arxiv]

    • Domain generalization in robust invariant representation

Updated at 2023-04-10:

  • Beyond Empirical Risk Minimization: Local Structure Preserving Regularization for Improving Adversarial Robustness [arxiv]

    • Local structure preserving for adversarial robustness 通过保留局部结构来进行对抗鲁棒性

  • TFS-ViT: Token-Level Feature Stylization for Domain Generalization [arxiv]

    • Token-level feature stylization for domain generalization 用token-level特征变换进行domain generalization

  • Are Data-driven Explanations Robust against Out-of-distribution Data? [arxiv]

    • Data-driven explanations robust? 探索数据驱动的解释是否是OOD鲁棒的

  • ERM++: An Improved Baseline for Domain Generalization [arxiv]

    • Improved ERM for domain generalization 提高的ERM用于domain generalization

Updated at 2023-04-04:

  • CVPR'23 Feature Alignment and Uniformity for Test Time Adaptation [arxiv]

    • Feature alignment for test-time adaptation 使用特征对齐进行测试时adaptation

  • Finding Competence Regions in Domain Generalization [arxiv]

    • Finding competence regions in domain generalization 在DG中发现能力区域

  • CVPR'23 TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization [arxiv]

    • Improve generalization and adversarial robustness 同时提高鲁棒性和泛化性

  • CVPR'23 Trainable Projected Gradient Method for Robust Fine-tuning [arxiv]

    • Trainable PGD for robust fine-tuning 可训练的pgd用于鲁棒的微调技术

  • Parameter-Efficient Tuning Makes a Good Classification Head [arxiv]

    • Parameter-efficient tuning makes a good classification head 参数高效的迁移学习成就一个好的分类头

  • Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning [arxiv]

    • Continual domain shift learning using adaptation and generalization 使用 adaptation和DG进行持续分布变化的学习

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.

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

http://transferlearning.xyz/

License:MIT License

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