Summary of The Expanding Scope Of the Stability Gap: Unveiling Its Presence in Joint Incremental Learning Of Homogeneous Tasks, by Sandesh Kamath et al.
The Expanding Scope of the Stability Gap: Unveiling its Presence in Joint Incremental Learning of Homogeneous Tasks
by Sandesh Kamath, Albin Soutif-Cormerais, Joost van de Weijer, Bogdan Raducanu
First submitted to arxiv on: 7 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 investigates the phenomenon of stability gap, where a temporary performance drop occurs when transitioning from one task to another in continual learning scenarios. The authors show that this drop also happens when joint incremental training is applied to homogeneous tasks, and identify a low-loss linear path to the next minima. However, they find that SGD optimization does not choose this path, suggesting potential solutions directions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how machine learning models perform when switching from one task to another. They found that there’s a problem called the “stability gap” where the model’s performance drops temporarily. This is important because it makes it harder for machines to learn new things and keeps them from being as efficient as they could be. The researchers also discovered that even when the model has access to all the data, it still doesn’t find the best way to improve. |
Keywords
» Artificial intelligence » Continual learning » Machine learning » Optimization
