迁移学习 Transfer Learning

Everything about Transfer Learning.

Papers • Tutorials • Research areas • Theory • Survey • Code • Dataset & benchmark

Thesis • Scholars • Contests • Journal/conference • Applications • Others • Contributing

Widely used by top conferences and journals:

Conferences: [NeurIPS'21] [IJCAI'21] [ESEC/FSE'20] [IJCNN'20] [ACMMM'18] [ICME'19]
Journals: [IEEE TKDE] [ACM TIST] [Information sciences] [Neurocomputing] [IEEE Transactions on Cognitive and Developmental Systems]

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

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:

By topic: doc/awesome_papers.md
By date: [2022-05] [2022-04] [2022-03] [2022-02] [2022-01] [2021-12] [2021-11] [2021-10] [2021-09] [2021-08] [2021-07]

Updated at 2022-06-24:

FL-IJCAI-22 MetaFed: Federated Learning among Federations with Cyclic Knowledge Distillation for Personalized Healthcare
- MetaFed: a new form of federated learning 联邦之联邦学习、新范式
Interspeech-22 Decoupled Federated Learning for ASR with Non-IID Data
- Decoupled federated learning for non IID 解耦的联邦架构用于Non-IID语音识别

Updated at 2022-06-23:

Few-Max: Few-Shot Domain Adaptation for Unsupervised Contrastive Representation Learning
- Few-shot DA for unsupervised constrastive learning 小样本DA用于无监督对比学习
The Importance of Background Information for Out of Distribution Generalization
- Background information for OOD generalization 背景信息对于OOD泛化的重要性
Zero-Shot AutoML with Pretrained Models
- 用预训练模型进行零样本的自动机器学习
How robust are pre-trained models to distribution shift?
- How robust are pre-trained models to distribution shift 评估预训练模型对于distribution shift的鲁棒性
FiT: Parameter Efficient Few-shot Transfer Learning for Personalized and Federated Image Classification
- Few-shot transfer learning for image classification 小样本迁移学习用于图像分类
COVID-19 Detection using Transfer Learning with Convolutional Neural Network
- COVID-19 using transfer learning 用迁移学习进行COVID-19检测

Updated at 2022-06-14:

Wav2vec-S: Semi-Supervised Pre-Training for Speech Recognition
- Pretraining for speech recognition 用预训练模型进行语音识别
Causal Balancing for Domain Generalization
- Causal balancing for domain generalization 因果平衡用于领域泛化
NAACL-22 Modularized Transfer Learning with Multiple Knowledge Graphs for Zero-shot Commonsense Reasoning
- Transfer learning for zero-shot reasoning 迁移学习用于零次常识推理
ConFUDA: Contrastive Fewshot Unsupervised Domain Adaptation for Medical Image Segmentation
- Fewshot UDA for medical image segmentation 小样本域自适应用于医疗图像分割
One Ring to Bring Them All: Towards Open-Set Recognition under Domain Shift
- Open set recognition with domain shift 开放集+domain shift
Toward Certified Robustness Against Real-World Distribution Shifts
- Certified robustness against real-world distribution shifts 真实世界中的distribution shift

Updated at 2022-06-10:

On Transfer Learning in Functional Linear Regression
- Transfer learning in functional linear regression 迁移学习用于函数式线性回归

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。

Books 书籍
- 《迁移学习》（杨强） [Buy] [English version]
- 《迁移学习导论》(王晋东、陈益强著) [Homepage] [Buy]
Blogs 博客
- Zhihu blogs - 知乎专栏《小王爱迁移》系列文章
Video tutorials 视频教程
Brief introduction and slides 简介与ppt资料
- Recent advance of transfer learning
- Domain generalization survey
- Brief introduction in Chinese
  - PPT (English) | PPT (中文)
- 迁移学习中的领域自适应方法 Domain adaptation: PDF ｜ Video on Bilibili | Video on Youtube
- Tutorial on transfer learning by Qiang Yang: IJCAI'13 | 2016 version
Talk is cheap, show me the code 动手教程、代码、数据
- Pytorch tutorial on transfer learning
Transfer Learning Scholars and Labs - 迁移学习领域的著名学者、代表工作及实验室介绍
Negative transfer - 负迁移

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)：

2022 Transfer Learning for Future Wireless Networks: A Comprehensive Survey
2022 A Review of Deep Transfer Learning and Recent Advancements
2022 Transferability in Deep Learning: A Survey, from Mingsheng Long in THU.
2021 Domain generalization: IJCAI-21 Generalizing to Unseen Domains: A Survey on Domain Generalization | 知乎文章 | 微信公众号
- First survey on domain generalization
- 第一篇对Domain generalization (领域泛化)的综述
2021 Vision-based activity recognition: A Survey of Vision-Based Transfer Learning in Human Activity Recognition
2021 ICSAI A State-of-the-Art Survey of Transfer Learning in Structural Health Monitoring
2020 Transfer learning: survey and classification, Advances in Intelligent Systems and Computing.
2020 迁移学习最新survey，来自中科院计算所庄福振团队，发表在Proceedings of the IEEE: A Comprehensive Survey on Transfer Learning
2020 负迁移的综述：Overcoming Negative Transfer: A Survey
2020 知识蒸馏的综述: Knowledge Distillation: A Survey
用transfer learning进行sentiment classification的综述：A Survey of Sentiment Analysis Based on Transfer Learning
2019 一篇新survey：Transfer Adaptation Learning: A Decade Survey
2018 一篇迁移度量学习的综述: Transfer Metric Learning: Algorithms, Applications and Outlooks
2018 一篇最近的非对称情况下的异构迁移学习综述：Asymmetric Heterogeneous Transfer Learning: A Survey
2018 Neural style transfer的一个survey：Neural Style Transfer: A Review
2018 深度domain adaptation的一个综述：Deep Visual Domain Adaptation: A Survey
2017 多任务学习的综述，来自香港科技大学杨强团队：A survey on multi-task learning
2017 异构迁移学习的综述：A survey on heterogeneous transfer learning
2017 跨领域数据识别的综述：Cross-dataset recognition: a survey
2016 A survey of transfer learning。其中交代了一些比较经典的如同构、异构等学习方法代表性文章。
2015 中文综述：迁移学习研究进展
2010 A survey on transfer learning
Survey on applications - 应用导向的综述：
- 视觉domain adaptation综述：Visual Domain Adaptation: A Survey of Recent Advances
- 迁移学习应用于行为识别综述：Transfer Learning for Activity Recognition: A Survey
- 迁移学习与增强学习：Transfer Learning for Reinforcement Learning Domains: A Survey
- 多个源域进行迁移的综述：A Survey of Multi-source Domain Adaptation。

Theory （理论文章）:

ICML-20 Few-shot domain adaptation by causal mechanism transfer
- The first work on causal transfer learning
- 日本理论组大佬Sugiyama的工作，causal transfer learning
CVPR-19 Characterizing and Avoiding Negative Transfer
- Characterizing and avoid negative transfer
- 形式化并提出如何避免负迁移
ICML-20 On Learning Language-Invariant Representations for Universal Machine Translation
- Theory for universal machine translation
- 对统一机器翻译模型进行了理论论证
NIPS-06 Analysis of Representations for Domain Adaptation
ML-10 A Theory of Learning from Different Domains
NIPS-08 Learning Bounds for Domain Adaptation
COLT-09 Domain adaptation: Learning bounds and algorithms
MMD paper：A Hilbert Space Embedding for Distributions and A Kernel Two-Sample Test
Multi-kernel MMD paper: Optimal kernel choice for large-scale two-sample tests

4.Code (代码)

Unified codebases for:

Deep domain adaptation
Deep domain generalization
See all codes here: https://github.com/jindongwang/transferlearning/tree/master/code.

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)

Visual Domain Adaptation Challenge (VisDA)

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)

Call for papers:
- Advances in Transfer Learning: Theory, Algorithms, and Applications, DDL: October 2021
Related projects:
- Salad: A semi-supervised domain adaptation library

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.

zlh-tobeornottobe / transferlearning Goto Github PK

transferlearning's Introduction