Summary of Sharpness-aware Minimization with Adaptive Regularization For Training Deep Neural Networks, by Jinping Zou et al.
Sharpness-Aware Minimization with Adaptive Regularization for Training Deep Neural Networks
by Jinping Zou, Xiaoge Deng, Tao Sun
First submitted to arxiv on: 22 Dec 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 Sharpness-Aware Minimization (SAM) has been successful in improving model generalization in machine learning tasks. However, SAM relies on a fixed hyperparameter associated with regularization to characterize the sharpness of the model. Despite its effectiveness, research on adaptive regularization methods based on SAM is scarce. The proposed SAM with Adaptive Regularization (SAMAR) introduces a flexible rule to update the regularization parameter dynamically. This approach ensures convergence for functions satisfying Lipschitz continuity and enhances accuracy and generalization in image recognition tasks using CIFAR-10 and CIFAR-100 datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way of making machine learning models better is called Sharpness-Aware Minimization (SAM). It helps models work well on new, unseen data. SAM uses a special number to make the model more careful or not, but this number doesn’t change. Researchers didn’t study how to change this number based on SAM, so they created a new method that does just that. They called it SAM with Adaptive Regularization (SAMAR). It changes the number based on the task and makes sure the model is good at recognizing images using two special datasets: CIFAR-10 and CIFAR-100. |
Keywords
» Artificial intelligence » Generalization » Hyperparameter » Machine learning » Regularization » Sam
