Summary of Adaptive Memory Replay For Continual Learning, by James Seale Smith et al.
Adaptive Memory Replay for Continual Learning
by James Seale Smith, Lazar Valkov, Shaunak Halbe, Vyshnavi Gutta, Rogerio Feris, Zsolt Kira, Leonid Karlinsky
First submitted to arxiv on: 18 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); 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 A novel framework for adaptive memory replay is introduced in this paper, addressing the challenge of catastrophic forgetting in foundation models (FMs) when only a limited amount of past data can be stored. The authors propose a multi-armed bandit approach to dynamically select past data for training based on the current task, using Bolzmann sampling to optimize the selection process. This framework is demonstrated to maintain high performance and reduce forgetting by up to 10% at no additional training cost in both vision and language pre-training tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Foundation models are an essential part of AI research, but they can be expensive to train. When we update these models with new data, it’s important not to forget what we’ve learned before. This is called catastrophic forgetting. The authors of this paper focus on a situation where we have all the past data, but not enough computer power to use it all. They show that a simple method can be more effective than traditional approaches in this setting. By using a new framework that selects which past data to use based on the current task, they are able to maintain good performance while reducing forgetting. |
