Skip to the content.

迁移学习 Transfer Learning

Awesome MIT License LICENSE 996.icu

Everything about Transfer Learning (Probably the most complete repository?). Your contribution is highly valued! If you find this repo helpful, please cite it as follows:

关于迁移学习的所有资料,包括:介绍、综述文章、最新文章、代表工作及其代码、常用数据集、硕博士论文、比赛等等。(可能是目前最全的迁移学习资料库?) 欢迎一起贡献! 如果认为本仓库有用,请在你的论文和其他出版物中进行引用!

@Misc{transferlearning.xyz,
howpublished = {\url{http://transferlearning.xyz}},   
title = {Everything about Transfer Learning and Domain Adapation},  
author = {Wang, Jindong and others}  
}  
Contents
0.Papers (论文) 1.Introduction and Tutorials (简介与教程)
2.Transfer Learning Areas and Papers (研究领域与相关论文) 3.Theory and Survey (理论与综述)
4.Code (代码) 5.Transfer Learning Scholars (著名学者)
6.Transfer Learning Thesis (硕博士论文) 7.Datasets and Benchmarks (数据集与评测结果)
8.Transfer Learning Challenges (迁移学习比赛) Applications (迁移学习应用)
Other Resources (其他资源) Contributing (欢迎参与贡献)

关于机器学习和行为识别的资料,请参考:行为识别机器学习


NOTE: You can directly open the code in Gihub Codespaces on the web to run them without downloading!

0.Papers (论文)

Latest papers (2021-07-28) - [Toward Co-creative Dungeon Generation via Transfer Learning](http://arxiv.org/abs/2107.12533) - Game scene generation with transfer learning - 用迁移学习生成游戏场景 - [Transfer Learning in Electronic Health Records through Clinical Concept Embedding](https://arxiv.org/abs/2107.12919) - Transfer learning in electronic health record - 迁移学习用于医疗记录管理
Latest papers (2021-07-27) - CVPR-21 [Conditional Bures Metric for Domain Adaptation](https://openaccess.thecvf.com/content/CVPR2021/html/Luo_Conditional_Bures_Metric_for_Domain_Adaptation_CVPR_2021_paper.html) - A new metric for domain adaptation - 提出一个新的metric用于domain adaptation - CVPR-21 [Wasserstein Barycenter for Multi-Source Domain Adaptation](https://openaccess.thecvf.com/content/CVPR2021/html/Montesuma_Wasserstein_Barycenter_for_Multi-Source_Domain_Adaptation_CVPR_2021_paper.html) - Use Wasserstein Barycenter for multi-source domain adaptation - 利用Wasserstein Barycenter进行DA - CVPR-21 [Generalized Domain Adaptation](https://openaccess.thecvf.com/content/CVPR2021/html/Mitsuzumi_Generalized_Domain_Adaptation_CVPR_2021_paper.html) - A general definition for domain adaptation - 一个更抽象更一般的domain adaptation定义 - CVPR-21 [Reducing Domain Gap by Reducing Style Bias](https://openaccess.thecvf.com/content/CVPR2021/html/Nam_Reducing_Domain_Gap_by_Reducing_Style_Bias_CVPR_2021_paper.html) - Syle-invariant training for adaptation and generalization - 通过训练图像对style无法辨别来进行DA和DG - CVPR-21 [Uncertainty-Guided Model Generalization to Unseen Domains](https://openaccess.thecvf.com/content/CVPR2021/html/Qiao_Uncertainty-Guided_Model_Generalization_to_Unseen_Domains_CVPR_2021_paper.html) - Uncertainty-guided generalization - 基于不确定性的domain generalization - CVPR-21 [Adaptive Methods for Real-World Domain Generalization](https://openaccess.thecvf.com/content/CVPR2021/html/Dubey_Adaptive_Methods_for_Real-World_Domain_Generalization_CVPR_2021_paper.html) - Adaptive methods for domain generalization - 动态算法,用于domain generalization
Latest papers (2021-07-16) - 20210716 ICML-21 [Continual Learning in the Teacher-Student Setup: Impact of Task Similarity](https://arxiv.org/abs/2107.04384) - Investigating task similarity in teacher-student learning - 调研在continual learning下teacher-student learning问题的任务相似度 - 20210716 BMCV-extend [Exploring Dropout Discriminator for Domain Adaptation](https://arxiv.org/abs/2107.04231) - Using multiple discriminators for domain adaptation - 用分布估计代替点估计来做domain adaptation - 20210716 TPAMI-21 [Lifelong Teacher-Student Network Learning](https://arxiv.org/abs/2107.04689) - Lifelong distillation - 持续的知识蒸馏 - 20210716 MICCAI-21 [Few-Shot Domain Adaptation with Polymorphic Transformers](https://arxiv.org/abs/2107.04805) - Few-shot domain adaptation with polymorphic transformer - 用多模态transformer做少样本的domain adaptation - 20210716 InterSpeech-21 [Speech2Video: Cross-Modal Distillation for Speech to Video Generation](https://arxiv.org/abs/2107.04806) - Cross-model distillation for video generation - 跨模态蒸馏用于语音到video的生成 - 20210716 ICML-21 workshop [Leveraging Domain Adaptation for Low-Resource Geospatial Machine Learning](https://arxiv.org/abs/2107.04983) - Using domain adaptation for geospatial ML - 用domain adaptation进行地理空间的机器学习

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)


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.


[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.