Summary of Making Robust Generalizers Less Rigid with Soft Ascent-descent, by Matthew J. Holland et al.
Making Robust Generalizers Less Rigid with Soft Ascent-Descent
by Matthew J. Holland, Toma Hamada
First submitted to arxiv on: 7 Aug 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 an innovative approach to improve the performance of machine learning models on rare or difficult data points. Traditional methods focus on average performance, but in practice, we care about how well a model performs on these challenging examples at test time. The authors demonstrate that sharpness-aware minimization, which has been successful for image classification tasks using deep neural networks, can break down when applied to more diverse models. As an alternative, they introduce a new training criterion that penalizes poor loss concentration and show it can be combined with existing methods like CVaR or DRO to control tail emphasis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making machine learning models better at handling tricky data points. Right now, most models are trained to do well on average, but in real life, we want them to perform well even on the toughest examples. The authors try a new approach called sharpness-aware minimization, which helps for image classification tasks using deep neural networks. However, this method doesn’t work as well with more diverse models. To solve this problem, they propose a new way of training models that focuses on doing better on tough examples. |
Keywords
* Artificial intelligence * Image classification * Machine learning
