Summary of Tsvd: Bridging Theory and Practice in Continual Learning with Pre-trained Models, by Liangzu Peng et al.
TSVD: Bridging Theory and Practice in Continual Learning with Pre-trained Models
by Liangzu Peng, Juan Elenter, Joshua Agterberg, Alejandro Ribeiro, René Vidal
First submitted to arxiv on: 1 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 abstract presents a novel approach to continual learning (CL) that combines theoretical soundness with high performance. The goal of CL is to train models that can solve multiple tasks presented sequentially. Recent approaches have achieved strong performance by leveraging large pre-trained models, but lack theoretical guarantees. This paper aims to bridge the gap between theory and practice by designing a simple CL method that is both theoretically sound and highly performant. The proposed approach, termed TSVD, lifts pre-trained features into a higher dimensional space and formulates an over-parametrized minimum-norm least-squares problem. To address challenges related to numerical instability and increased generalization errors, the method continually truncates the singular value decomposition (SVD) of the lifted features. TSVD is stable with respect to hyperparameters, can handle hundreds of tasks, and outperforms state-of-the-art CL methods on multiple datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a simple yet powerful approach to continual learning that combines theory and practice. The goal is to train models that can solve multiple tasks presented sequentially, which recent approaches have achieved through pre-trained models. However, these methods lack theoretical guarantees. This paper aims to bridge this gap by designing an approach called TSVD, which lifts pre-trained features into a higher dimensional space. This allows the model to learn from new tasks while avoiding numerical instability and increased generalization errors. |
Keywords
» Artificial intelligence » Continual learning » Generalization
