jianghaojun/Awesome-Parameter-Efficient-Transfer-Learning
A curated collection of research papers on parameter-efficient fine-tuning methods for vision and multimodal AI models.
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This repository aggregates academic papers on parameter-efficient transfer learning techniques, including prompt tuning, adapter methods, and unified approaches. It targets researchers and practitioners working with large-scale vision-language models who want to adapt these models efficiently for downstream tasks without full fine-tuning.
Frequently asked
- What is jianghaojun/Awesome-Parameter-Efficient-Transfer-Learning?
- A curated collection of research papers on parameter-efficient fine-tuning methods for vision and multimodal AI models.
- Is Awesome-Parameter-Efficient-Transfer-Learning open source?
- Yes — jianghaojun/Awesome-Parameter-Efficient-Transfer-Learning is open source, released under the MIT license.
- How popular is Awesome-Parameter-Efficient-Transfer-Learning?
- jianghaojun/Awesome-Parameter-Efficient-Transfer-Learning has 409 stars on GitHub.
- Where can I find Awesome-Parameter-Efficient-Transfer-Learning?
- jianghaojun/Awesome-Parameter-Efficient-Transfer-Learning is on GitHub at https://github.com/jianghaojun/Awesome-Parameter-Efficient-Transfer-Learning.