  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. Experimental
results in various benchmark datasets show that ASAM contributes to significant
improvement of model generalization performance.
