Guide · Open source
How to use transferlearning: clone the repo and find papers, tutorials and code
Updated · written with AI from the project's own documentation and checked against our catalogue data
transferlearning is a large open-source reference repository by Jindong Wang about transfer learning, where a model trained on one task or data distribution is reused for another. It gathers research papers grouped by subtopic, books, video tutorials, datasets and some runnable code toolboxes. This guide shows you how to get a local copy, where to find each kind of material, and which parts are worth your time.
transferlearningjindongwang/transferlearning · ★ 14.4k · MIT Install options & downloads Quick start
- 1Clone the repository. Run git clone https://github.com/jindongwang/transferlearning to get a local copy. Or open it in GitHub Codespaces to skip downloading.
- 2Pick your goal. Decide whether you need reading material, research papers or runnable code, then go to the matching section in the README.
- 3Read a tutorial or book. Start with the Introduction and Tutorials section for books, videos and slides, many of them in Chinese.
- 4Browse papers by topic. Open the awesome paper list by topic, such as domain adaptation or domain generalization, to find relevant work.
- 5Open a code toolbox. Go to code/DeepDA or code/DeepDG and read the README in that folder for its setup and run steps.
- 6Verify before you rely on it. Check paper dates, linked external resources and the status of any code, since the README's latest entries are dated inconsistently.
What transferlearning is and what it is not
transferlearning is a collection of resources about transfer learning, domain adaptation, domain generalization and multi-task learning. Its README describes it as 'Everything about Transfer Learning. 迁移学习.' It is best read as a map of the field, not as a single program you run to get results.
The repository is written mainly in Python and is licensed under MIT.
- It is a reading list and resource hub, not a finished application.
- It includes some runnable code toolboxes such as DeepDA and DeepDG.
- Much of the material links out to external books, videos and slides.
Get a local copy of the repository
The README lists a clone-and-build install method and names no prerequisites. To get a local copy, clone the repository with the command below. The README also notes that you can open the code in GitHub Codespaces on the web to run it without downloading, or try github.dev.
git clone https://github.com/jindongwang/transferlearningFind papers organized by topic
The Papers section links to an awesome transfer learning papers list and to two views of the latest papers: one by topic (doc/awesome_papers.md) and one by date (doc/awesome_paper_date.md). Research areas such as domain adaptation and domain generalization are listed in section 2, with links to surveys, theory and fine-tuning work.
Check the dates on the latest-paper entries yourself. The README's entries are dated inconsistently, so treat the newest additions with care.
- Start with the topic list if you want a subfield such as domain adaptation.
- Use the date list if you want to scan recent additions.
Learn the basics from books, videos and slides
The Introduction and Tutorials section is the best starting point for a beginner. It lists books, including Introduction to Transfer Learning: Algorithms and Practice and two Chinese-language books, plus a Zhihu blog series, Bilibili and YouTube video tutorials, and slide decks on recent advances, domain generalization and domain adaptation.
Many of these resources are in Chinese, which helps Chinese-speaking learners. Some are external links that you may need to buy or access elsewhere.
- Books: an English book from Springer and Yang Qiang's book in Chinese and English.
- Videos: Chinese-language lectures, including one by Hung-yi Lee of NTU.
- Slides: a domain generalization survey deck and a domain adaptation PDF.
Run the reference code for deep domain adaptation
The Code section links to the code folder of the repository. The README names two toolboxes: DeepDA, a unified deep domain adaptation toolbox, and DeepDG, a unified deep domain generalization toolbox. It also links a PyTorch fine-tuning example in code/AlexNet_ResNet and the official PyTorch transfer learning tutorial.
After cloning, open the code folder and read the README in each toolbox folder for its own setup and run steps. This guide does not list those commands, because the source README does not include them.
- DeepDA: code/DeepDA
- DeepDG: code/DeepDG
- Fine-tuning example: code/AlexNet_ResNet
- More code: the code folder on GitHub
Check the datasets and related projects
The repository has a Datasets and benchmarks section. The README also points to related projects from the same author, covering areas such as federated learning and activity recognition, along with Microsoft projects for robust learning and semi-supervised learning. These are separate repositories with their own licences and status.
- Use the Datasets section to find data for experiments.
- Check each linked project's own page before relying on it.
What people use it for
- A student building a reading list on domain adaptation from the tutorial and slide links.
- A researcher scanning papers grouped by subtopic such as domain generalization.
- A developer starting from the DeepDA toolbox to set up deep domain adaptation experiments.
- A Chinese-speaking learner using the Chinese books, Bilibili videos and Zhihu blog series.
Frequently asked questions
How do I install transferlearning?
The README uses a clone-and-build method. Run git clone https://github.com/jindongwang/transferlearning to get the repository. The README does not list other install prerequisites, and each code toolbox has its own folder and instructions.
Is transferlearning free to use?
The repository is licensed under MIT, which is listed in the README and its badge. The external books, videos and papers it links to have their own terms.
Is transferlearning a Python library I can import?
No. It is a collection of papers, books, tutorials, datasets and code toolboxes. The language is Python, and the DeepDA and DeepDG toolboxes are the runnable code it contains.
What is the difference between DeepDA and DeepDG?
According to the README, DeepDA is a unified deep domain adaptation toolbox and DeepDG is a unified deep domain generalization toolbox. Use DeepDA for domain adaptation experiments and DeepDG for domain generalization experiments.
Is transferlearning up to date?
The README's latest paper entries are dated inconsistently, and the repository's last push was in February 2025. Check the dates of individual papers and links before relying on them for current results.
What are good alternatives to transferlearning?
The comparable projects listed are awesome-domain-adaptation, DeepDA and Transfer-Learning-Library. Choose awesome-domain-adaptation for a reading list focused on domain adaptation, or Transfer-Learning-Library if you want a code library instead of a collection of resources.
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Our verdict
Use transferlearning if you want a broad map of transfer learning, with reading lists, Chinese-language tutorials and reference code such as DeepDA and DeepDG. Pick a different resource if you need a step-by-step course, a finished application, or guaranteed up-to-date results, since most material links out and some entries are dated inconsistently.
Install options & downloads