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Orion514 / 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 repos：[USB: unified semi-supervised learning benchmark] | [TorchSSL: a unified SSL library] | [PersonalizedFL: library for personalized federated 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:

Updated at 2022-09-29:

  • Assaying Out-Of-Distribution Generalization in Transfer Learning [arXiv]

    • A lot of experiments to show OOD performance

  • ICML-21 Accuracy on the Line: on the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization [arxiv]

    • Strong correlation between ID and OOD

Updated at 2022-09-26:

  • Deep Domain Adaptation for Detecting Bomb Craters in Aerial Images [arxiv]

    • Bomb craters detection using domain adaptation 用DA检测遥感图像中的炮弹弹坑

  • WACV-23 TeST: Test-time Self-Training under Distribution Shift [arxiv]

    • Test-time self-training 测试时训练

  • StyleTime: Style Transfer for Synthetic Time Series Generation [arxiv]

    • Style transfer for time series generation 时间序列生成的风格迁移

  • Robust Domain Adaptation for Machine Reading Comprehension [arxiv]

    • Domain adaptation for machine reading comprehension 机器阅读理解的domain adaptation

Updated at 2022-09-18:

  • Generalized representations learning for time series classification [arxiv]

    • OOD for time series classification 域泛化用于时间序列分类

  • USB: A Unified Semi-supervised Learning Benchmark [arxiv] [code]

    • Unified semi-supervised learning codebase 半监督学习统一代码库

  • Test-Time Training with Masked Autoencoders [arxiv]

    • Test-time training with MAE MAE的测试时训练

  • Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models [arxiv]

    • Test-time prompt tuning 测试时的prompt tuning

Updated at 2022-09-13:

  • TeST: test-time self-training under distribution shift [arxiv]

    • Test-time self-training 测试时的self-training

  • Language-aware Domain Generalization Network for Cross-Scene Hyperspectral Image Classification [arxiv]

    • Domain generalization for cross-scene hyperspectral image classification 域泛化用于高光谱图像分类

  • IEEE-TMM'22 Uncertainty Modeling for Robust Domain Adaptation Under Noisy Environments [IEEE]

    • Uncertainty modeling for domain adaptation 噪声环境下的domain adaptation

Updated at 2022-09-07:

  • Improving Robustness to Out-of-Distribution Data by Frequency-based Augmentation arxiv

    • OOD by frequency-based augmentation 通过基于频率的数据增强进行OOD

  • Domain Generalization for Prostate Segmentation in Transrectal Ultrasound Images: A Multi-center Study arxiv

    • Domain generalizationfor prostate segmentation 领域泛化用于前列腺分割

  • Domain Adaptation from Scratch arxiv

    • Domain adaptation from scratch

  • Towards Optimization and Model Selection for Domain Generalization: A Mixup-guided Solution arxiv

    • Model selection for domain generalization 域泛化中的模型选择问题

Updated at 2022-09-01:

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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