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Contributors Forks Stargazers Issues

Transfer Leanring

Everything about Transfer Learning. 迁移学习.

PapersTutorialsResearch areasTheorySurveyCodeDataset & benchmark


Widely used by top conferences and journals:

howpublished = {\url{}},   
title = {Everything about Transfer Learning and Domain Adapation},  
author = {Wang, Jindong and others}  

Awesome MIT License LICENSE

Related Codes:

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

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 2023-03-23:

  • CVPR'23 A New Benchmark: On the Utility of Synthetic Data with Blender for Bare Supervised Learning and Downstream Domain Adaptation [arxiv]

    • A new benchmark for domain adaptation 一个对于domain adaptation最新的benchmark
  • Unsupervised domain adaptation by learning using privileged information [arxiv]

    • Domain adaptation by privileged information 使用高级信息进行domain adaptation
  • A Unified Continual Learning Framework with General Parameter-Efficient Tuning [arxiv]

    • A continual learning framework for parameter-efficient tuning 一个对于参数高效迁移的连续学习框架
  • CVPR'23 Sharpness-Aware Gradient Matching for Domain Generalization [arxiv]

    • Sharpness-aware gradient matching for DG 利用梯度匹配进行domain generalization
  • TempT: Temporal consistency for Test-time adaptation [arxiv]

    • Temporeal consistency for test-time adaptation 时间一致性用于test-time adaptation
  • TMLR'23 Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks [arxiv]

    • A framework for online learning 一个在线学习的框架
  • ICLR'23 workshop SPDF: Sparse Pre-training and Dense Fine-tuning for Large Language Models [arxiv]

    • Sparse pre-training and dense fine-tuning
  • CVPR'23 ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization [arxiv]

    • A lightweight module for domain generalization 一个用于DG的轻量级模块
  • ICLR'23 Contrastive Alignment of Vision to Language Through Parameter-Efficient Transfer Learning [arxiv]

    • Contrastive alignment for vision language models using transfer learning 使用参数高效迁移进行视觉语言模型的对比对齐
  • Probabilistic Domain Adaptation for Biomedical Image Segmentation [arxiv]

    • Probabilistic domain adaptation for biomedical image segmentation 概率的domain adaptation用于生物医疗图像分割

Updated at 2023-03-15:

  • Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology [arxiv]

    • Imbalanced domain generalization for single cell classification 不平衡的DG用于单细胞分类
  • Revisit Parameter-Efficient Transfer Learning: A Two-Stage Paradigm [arxiv]

    • Parameter-efficient transfer learning: a two-stage approach 一种两阶段的参数高效迁移学习
  • Unsupervised Cumulative Domain Adaptation for Foggy Scene Optical Flow [arxiv]

    • Domain adaptation for foggy scene optical flow 领域自适应用于雾场景的光流
  • ICLR'23 AutoTransfer: AutoML with Knowledge Transfer -- An Application to Graph Neural Networks [arxiv]

    • GNN with autoML transfer learning 用于GNN的自动迁移学习
  • Transfer Learning for Real-time Deployment of a Screening Tool for Depression Detection Using Actigraphy [arxiv]

    • Transfer learning for Depression detection 迁移学习用于脉动计焦虑检测
  • Domain Generalization via Nuclear Norm Regularization [arxiv]

    • Domain generalization via nuclear norm regularization 使用核归一化进行domain generalization

Updated at 2023-03-07:

  • To Stay or Not to Stay in the Pre-train Basin: Insights on Ensembling in Transfer Learning [arxiv]

    • Ensembling in transfer learning 调研迁移学习中的集成
  • CVPR'13 Masked Images Are Counterfactual Samples for Robust Fine-tuning [arxiv]

    • Masked images for robust fine-tuning 调研masked image对于fine-tuning的影响
  • FedCLIP: Fast Generalization and Personalization for CLIP in Federated Learning [arxiv]

    • Fast generalization for federated CLIP 在联邦中进行快速的CLIP训练

Updated at 2023-03-02:

  • Robust Representation Learning with Self-Distillation for Domain Generalization [arxiv]

    • Robust representation learning with self-distillation
  • ICLR-23 Temporal Coherent Test-Time Optimization for Robust Video Classification [arxiv]

    • Temporal distribution shift in video classification
  • WSDM-23 A tutorial on domain generalization [link] | [website]

    • A tutorial on domain generalization

Updated at 2023-02-23:

  • On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective [arxiv] | [code]
    • Adversarial and OOD evaluation of ChatGPT 对ChatGPT鲁棒性的评测

Updated at 2023-02-08:

  • Transfer learning for process design with reinforcement learning [arxiv]

    • Transfer learning for process design with reinforcement learning 使用强化迁移学习进行过程设计
  • Domain Adaptation for Time Series Under Feature and Label Shifts [arxiv]

    • Domain adaptation for time series 用于时间序列的domain adaptation
  • How Reliable is Your Regression Model's Uncertainty Under Real-World Distribution Shifts? [arxiv]

    • Regression models uncertainty for distribution shift 回归模型对于分布漂移的不确定性

Updated at 2023-02-02:

  • ICLR'23 SoftMatch: Addressing the Quantity-Quality Tradeoff in Semi-supervised Learning [arxiv]

    • Semi-supervised learning algorithm 解决标签质量问题的半监督学习方法
  • Empirical Study on Optimizer Selection for Out-of-Distribution Generalization [arxiv]

    • Opimizer selection for OOD generalization OOD泛化中的学习器选择
  • ICML'22 Understanding the failure modes of out-of-distribution generalization [arxiv]

    • Understand the failure modes of OOD generalization 探索OOD泛化中的失败现象
  • ICLR'23 Out-of-distribution Representation Learning for Time Series Classification [arxiv]

    • OOD for time series classification 时间序列分类的OOD算法

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.


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.


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.


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.