Summary of Layerwise Proximal Replay: a Proximal Point Method For Online Continual Learning, by Jason Yoo et al.
Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning
by Jason Yoo, Yunpeng Liu, Frank Wood, Geoff Pleiss
First submitted to arxiv on: 14 Feb 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 This paper explores the limitations of experience replay in online continual learning. Experience replay is a common technique used to prevent catastrophic forgetting and underfitting on past data. However, researchers have found that neural networks trained with experience replay tend to have unstable optimization trajectories, which can impede their overall accuracy. The authors of this paper identify this limitation and propose a simple modification to the optimization geometry called Layerwise Proximal Replay (LPR). LPR balances learning from new and replay data while only allowing for gradual changes in the hidden activation of past data. As a result, LPR consistently improves replay-based online continual learning methods across multiple problem settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computers learn from new information without forgetting what they already know. When computers try to learn from lots of different things, they can get confused and forget old information. One way people try to solve this is by using something called experience replay. But researchers found that even when computers have all the old information available, their learning process can still be unstable. The authors of this paper came up with a new idea called Layerwise Proximal Replay (LPR) that helps computers learn better from new and old information. |
Keywords
* Artificial intelligence * Continual learning * Optimization * Underfitting
