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    README.md

    Contributors Forks Stargazers Issues


    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: [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 2022-11-14:

    • Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts [arxiv]

      • Normalization perturbation for domain generalization 通过归一化扰动来进行domain generalization

    • FIXED: Frustraitingly easy domain generalization using Mixup [arxiv]

      • 使用Mixup进行domain generalization

    • Learning to Learn Domain-invariant Parameters for Domain Generalization [arxiv]

      • Learning to learn domain-invariant parameters for domain generalization

    Updated at 2022-11-07:

    • NeurIPS'22 Improved Fine-Tuning by Better Leveraging Pre-Training Data [openreview]

      • Using pre-training data for fine-tuning 用预训练数据来做微调

    • NeurIPS'22 Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning [openreview]

      • Adaptive contrastive learning for source-free DA 自适应的对比学习用于source-free DA

    • NeurIPS'22 LOG: Active Model Adaptation for Label-Efficient OOD Generalization [openreview]

      • Model adaptation for label-efficient OOD generalization

    • NeurIPS'22 MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification [openreview]

      • Multi-model domain adaptation mor medical image classification 多模型DA用于医疗数据

    • NeurIPS'22 Domain Adaptation under Open Set Label Shift [openreview]

      • Domain adaptation under open set label shift 在开放集的label shift中的DA

    Updated at 2022-11-03:

    • NeurIPS'22 Domain Generalization without Excess Empirical Risk [openreview]

      • Domain generalization without excess empirical risk

    • NeurIPS'22 FedSR: A Simple and Effective Domain Generalization Method for Federated Learning [openreview]

      • FedSR for federated learning domain generalization 用于联邦学习的domain generalization

    • NeurIPS'22 Probable Domain Generalization via Quantile Risk Minimization [openreview]

      • Domain generalization with quantile risk minimization 用quantile风险最小化的domain generalization

    • NeurIPS'22 Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer [arxiv]

      • Continual learning with backward knowledge transfer 反向知识迁移的持续学习

    • NeurIPS'22 Test Time Adaptation via Conjugate Pseudo-labels [openreview]

      • Test-time adaptation with conjugate pseudo-labels 用伪标签进行测试时adaptation

    • NeurIPS'22 Your Out-of-Distribution Detection Method is Not Robust! [openreview]

      • OOD models are not robust 分布外泛化模型不够鲁棒

    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.

    项目简介

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

    🚀 Github 镜像仓库 🚀

    源项目地址 ⬇ ⬇ ⬇

    https://github.com/jindongwang/transferlearning

    发行版本

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    贡献者 36

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    开发语言

    • Python 86.2 %
    • MATLAB 4.2 %
    • Jupyter Notebook 3.6 %
    • Shell 3.2 %
    • Makefile 1.4 %