Summary of Mgser-sam: Memory-guided Soft Experience Replay with Sharpness-aware Optimization For Enhanced Continual Learning, by Xingyu Li and Bo Tang
MGSER-SAM: Memory-Guided Soft Experience Replay with Sharpness-Aware Optimization for Enhanced Continual Learning
by Xingyu Li, Bo Tang
First submitted to arxiv on: 15 May 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 paper proposes a novel algorithm called MGSER-SAM to address the catastrophic forgetting problem in continual learning (CL). It integrates the SAM optimizer with Experience Replay frameworks like ER and DER++ to enhance generalization capabilities. The algorithm reconciles conflicts between ongoing tasks and previously stored memories by strategically integrating soft logits and aligning memory gradient directions. Through experiments across multiple benchmarks, MGSER-SAM outperforms existing baselines in all three CL scenarios, achieving a 24.4% and 17.6% improvement in testing accuracy compared to ER and DER++ respectively. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how computers can learn new things without forgetting what they already know. It introduces an algorithm called MGSER-SAM that makes this happen by combining different ideas from the past with what’s happening now. This way, computers can get better at doing tasks without losing their skills. The experiment shows that MGSER-SAM works really well and beats other algorithms in many situations. |
Keywords
» Artificial intelligence » Continual learning » Generalization » Logits » Sam
