transferlearning
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Explore resources and tutorials about transfer learning and related topics in machine learning.
From the README
- Large language model evaluation: [llm-eval]
- Large language model enhancement: [llm-enhance]
- Robust machine learning: [robustlearn: robust machine learning]
Excerpts from the project README on GitHub. Copyright and licensing remain with the respective authors.
Claim its page: indexed whatever its rank, translated into six languages, and enriched with what you write yourself.
Get an email alert on its next release or when it starts trending — never miss the moment.
Free · no card · unsubscribe anytimeTransfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
transferlearning has 14.3k stars on GitHub. It has been forked 3.8k times. transferlearning is written mainly in Python. It has been in active development since 2017. transferlearning is available under the MIT license. Its main topics are deep-learning, domain-adaptation, domain-adaption, domain-generalization.
What is transferlearning?
Transfer learning / domain adaptation / domain generalization / multi-task learning etc. Papers, codes, datasets, applications, tutorials.-迁移学习
Is transferlearning open source?
transferlearning is an open-source project. It is released under the MIT license.
Is transferlearning free?
Yes. transferlearning is free and open source — you can use, modify and self-host it.
What license does transferlearning use?
transferlearning is available under the MIT license.
What language is transferlearning written in?
transferlearning is written mainly in Python.
Add this live badge to your README — your GitHub stars and directory rank, refreshed daily.
[](https://olud.ai/project/jindongwang-transferlearning.html)
Measured from GitHub topics shared by both projects, weighted by how rare each topic is.