Summary of Gradient Correlation Subspace Learning Against Catastrophic Forgetting, by Tammuz Dubnov et al.
Gradient Correlation Subspace Learning against Catastrophic Forgetting
by Tammuz Dubnov, Vishal Thengane
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); 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 The paper introduces Gradient Correlation Subspace Learning (GCSL), a novel method to reduce catastrophic forgetting in incremental class learning. GCSL detects the subspace of weights least affected by previous tasks and projects the weights to train for new tasks into this subspace. This approach can be applied to one or more layers of a given network architecture, with the size of the subspace used adjustable from layer to layer and task to task. The method aims to efficiently learn from incremental class updates while preserving knowledge of previously learned classes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps machines learn new things without forgetting what they already know. It’s called “catastrophic forgetting” when a machine forgets previous learning because of new data. To solve this problem, the researchers created a new method called Gradient Correlation Subspace Learning (GCSL). GCSL works by finding parts of the machine’s brain that are not affected by old information and using those parts to learn new things. |
