ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning of Deep Neural Networks

Understand

Recently, learning algorithms motivated from sharpness of loss surface as an effective measure of generalization gap have shown state-of-the-art performances.

• Nevertheless, sharpness defined in a rigid region with a fixed radius, has a drawback in sensitivity to parameter re-scaling which leaves the loss unaffected, leading to weakening of the connection between sharpness and generalization gap.
• In this paper, we introduce the concept of adaptive sharpness which is scale-invariant and propose the corresponding generalization bound.
• We suggest a novel learning method, adaptive sharpness-aware minimization (ASAM), utilizing the proposed generalization bound.