Summary of Adaptive Adversarial Cross-entropy Loss For Sharpness-aware Minimization, by Tanapat Ratchatorn et al.
Adaptive Adversarial Cross-Entropy Loss for Sharpness-Aware Minimization
by Tanapat Ratchatorn, Masayuki Tanaka
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Image and Video Processing (eess.IV)
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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 a novel approach to enhance model generalization by introducing an adaptive adversarial cross-entropy (AACE) loss function for sharpness-aware minimization (SAM). Building upon the concept that the sharpness of the loss surface is an effective measure for improving the generalization gap, SAM consists of two main steps: weight perturbation and updating. However, the perturbation in SAM is determined by only the gradient of the training loss, which becomes small and oscillates as the model approaches a stationary point. The authors introduce AACE to replace standard cross-entropy loss, ensuring consistent perturbation direction and addressing the gradient diminishing issue. The proposed approach demonstrates improved performance in image classification tasks using Wide ResNet and PyramidNet across various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper improves how machine learning models work by making them better at generalizing from small training sets to real-world scenarios. This is achieved by introducing a new way of calculating the loss function, which helps the model explore more possibilities near the optimal solution. The approach shows promising results in image classification tasks and can be applied to other areas where improved generalization is important. |
Keywords
» Artificial intelligence » Cross entropy » Generalization » Image classification » Loss function » Machine learning » Resnet » Sam
