Summary of An Efficient Replay For Class-incremental Learning with Pre-trained Models, by Weimin Yin and Bin Chen Adn Chunzhao Xie and Zhenhao Tan
An Efficient Replay for Class-Incremental Learning with Pre-trained Models
by Weimin Yin, Bin Chen adn Chunzhao Xie, Zhenhao Tan
First submitted to arxiv on: 15 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 new method to overcome catastrophic forgetting in class-incremental learning, which is achieved by retaining only a single sample unit of each class in memory for replay and applying simple gradient constraints. The method leverages pre-trained models and demonstrates competitive performance with low computational cost using cross-entropy loss. By disrupting the steady state among weights guided by each class center, the proposed approach mitigates catastrophic forgetting. This innovation has significant implications for continuous learning applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help machines learn new things without forgetting what they already know. Right now, most machine learning algorithms get stuck because they keep trying to memorize everything instead of focusing on what’s truly important. The researchers found that by keeping only one example of each thing they’ve learned and using simple rules to adjust how they make decisions, they can do better with less effort. This means we might be able to teach machines to learn new things without getting stuck in the past. |
Keywords
» Artificial intelligence » Cross entropy » Machine learning
