Summary of Fixed Random Classifier Rearrangement For Continual Learning, by Shengyang Huang and Jianwen Mo
Fixed Random Classifier Rearrangement for Continual Learning
by Shengyang Huang, Jianwen Mo
First submitted to arxiv on: 23 Feb 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 This research proposes a novel continual learning algorithm called Fixed Random Classifier Rearrangement (FRCR) to mitigate catastrophic forgetting in neural networks. The algorithm consists of two stages: first, it replaces learnable classifiers with fixed random ones, constraining the norm of equivalent one-class classifiers without affecting performance; second, it rearranges new classifier entries to implicitly reduce drift in old latent representations. Experimental results on multiple datasets demonstrate that FRCR effectively reduces model forgetting. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research is about helping neural networks remember what they learned earlier when learning something new. Right now, neural networks forget the things they knew before because of a problem called catastrophic forgetting. The researchers found that by changing how classifiers work, they can make the network remember better. They came up with an algorithm to do this, which has two parts: first, it makes some changes to the way the network classifies things; second, it helps keep old knowledge from getting lost. Tests on different datasets showed that this method really works! |
Keywords
* Artificial intelligence * Continual learning
