Summary of Self-expansion Of Pre-trained Models with Mixture Of Adapters For Continual Learning, by Huiyi Wang et al.
Self-Expansion of Pre-trained Models with Mixture of Adapters for Continual Learning
by Huiyi Wang, Haodong Lu, Lina Yao, Dong Gong
First submitted to arxiv on: 27 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes a novel approach called Self-Expansion of pre-trained models with Modularized Adaptation (SEMA) for continual learning. SEMA aims to balance stability and adaptability in pre-trained models (PTMs) by automatically deciding whether to reuse or add adapter modules based on the detection of significant distribution shifts at different representation levels. The proposed method consists of a modular adapter, including a functional adapter and a representation descriptor trained as a distribution shift indicator. An expandable weighting router is learned jointly for mixing adapter outputs. Experimental results demonstrate SEMA’s effectiveness in achieving state-of-the-art performance compared to PTM-based CL methods without memory rehearsal. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Continual learning is a way for machines to keep getting smarter without forgetting what they already know. This paper proposes a new method called Self-Expansion of pre-trained models with Modularized Adaptation (SEMA) that helps these machines learn and adapt better. SEMA works by deciding whether to reuse or add new modules based on how different the data is from what it’s learned before. The method has a special adapter that can adjust its own size and shape to fit the new data, which allows it to learn more efficiently. This approach leads to better results than previous methods without rehearsing what it already knows. |
Keywords
* Artificial intelligence * Continual learning
