Sharpness Aware Optimization

Sharpness-Aware Optimization (SAO) focuses on improving the generalization ability of deep learning models by minimizing the sharpness of the loss landscape around the model's parameters. Current research explores various SAO algorithms, including Sharpness-Aware Minimization (SAM) and its adaptive variants, applying them to diverse architectures like convolutional neural networks and transformers, and within continual learning frameworks. This approach shows promise in enhancing model robustness and performance across various domains, including medical image analysis and time series forecasting, particularly where data scarcity or distribution shifts are significant challenges.