Optimizing Deep Learning Models with SAM

Overparameterized deep learning models can generalize surprisingly well, a phenomenon linked to the geometry of the loss landscape they navigate during training. Models that converge to "flat" minima in this landscape tend to perform better on new data than those that settle into "sharp" minima. The Sharpness-Aware-Minimization (SAM) optimizer is designed to explicitly find these flatter, more generalizable solutions. Rather than just minimizing the loss, SAM first finds a "worst-case" perturbation that maximizes loss within a small neighborhood and then takes a gradient step to minimize that maximized loss, effectively pushing the model away from sharp regions.