【论文合集】Awesome Transfer Learning网站首页 其他

Multi-Source to Multi-Target Decentralized Federated Domain Adaptation [arxiv]
- Multi-source to multi-target federated domain adaptation 多源多目标的联邦域自适应
ICML'23 AdaNPC: Exploring Non-Parametric Classifier for Test-Time Adaptation [arxiv]
- Adaptive test-time adaptation 非参数化分类器进行测试时adaptation

Updated at 2023-04-23:

Improved Test-Time Adaptation for Domain Generalization [arxiv]
- Improved test-time adaptation for domain generalization
Reweighted Mixup for Subpopulation Shift [arxiv]
- Reweighted mixup for subpopulation shift

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。

Books 书籍
- Introduction to Transfer Learning: Algorithms and Practice [Buy or read]
- 《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
- Transfer learning 迁移学习:
- Domain generalization 领域泛化：
  - IJCAI-ECAI'22 tutorial on domain generalization - 领域泛化tutorial
  - Domain generalization - 迁移学习新兴研究方向领域泛化
- Domain adaptation 领域自适应：
  - Domain adaptation - 迁移学习中的领域自适应方法(中文)
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)：

2023 Source-Free Unsupervised Domain Adaptation: A Survey [arxiv]
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: transferlearning/code at master · jindongwang/transferlearning · GitHub.

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.