Summary of Effective Gradient Sample Size Via Variation Estimation For Accelerating Sharpness Aware Minimization, by Jiaxin Deng et al.
Effective Gradient Sample Size via Variation Estimation for Accelerating Sharpness aware Minimization
by Jiaxin Deng, Junbiao Pang, Baochang Zhang, Tian Wang
First submitted to arxiv on: 24 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 optimization technique to accelerate the Sharpness-aware Minimization (SAM) algorithm, which has been shown to improve model generalization ability. The authors analyze the gradient calculation process of SAM and discover that it can be broken down into two components: the stochastic gradient descent (SGD) gradient and the Projection of the Second-order gradient matrix onto the First-order gradient (PSF). They design an adaptive sampling method based on the variation of PSF, which significantly accelerates the training process. The proposed method achieves state-of-the-art accuracies comparable to SAM on various network architectures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study is about making a powerful machine learning tool called Sharpness-aware Minimization faster and more efficient. Right now, it takes too long to train because it has to calculate something twice. Researchers figured out that this calculation can be broken down into two parts: one part is like a standard training process, and the other part changes frequently during training. They created a new way to use this changing part to speed up the training process without losing accuracy. This new method works just as well as the original one on different types of networks. |
Keywords
* Artificial intelligence * Generalization * Machine learning * Optimization * Sam * Stochastic gradient descent
