Sharpness-Aware Minimization: Stop Chasing Zero Training Loss
Sharpness-Aware Minimization (SAM) is a powerful optimizer that fixes generalization issues in deep learning. Learn how to find flatter minima in the loss landscape, avoid the ‘sharp minima’ trap, and implement the algorithm in PyTorch while avoiding critical pitfalls like the BatchNorm statistics drift.