Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ASAM: Adaptive Sharpness-Aware Minimization for Scale-Invariant Learning
  of Deep Neural Networks   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.

0
The categories of this paper are: hep-ph
29
The categories of this paper are: funct-an
25
The categories of this paper are: patt-sol
13
The categories of this paper are: quant-ph
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
28
The categories of this paper are: alg-geom
12
The categories of this paper are: nucl-th
17
The categories of this paper are: q-fin
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
33
The categories of this paper are: plasm-ph
15
The categories of this paper are: nucl-ex