Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Sharpness-Aware Minimization with Dynamic Reweighting   Deep neural networks are often overparameterized and may not easily achieve
model generalization. Adversarial training has shown effectiveness in improving
generalization by regularizing the change of loss on top of adversarially
chosen perturbations. The recently proposed sharpness-aware minimization (SAM)
algorithm conducts adversarial weight perturbation, encouraging the model to
converge to a flat minima. Unfortunately, due to increased computational cost,
adversarial weight perturbation can only be efficiently estimated per-batch
instead of per-instance by SAM, leading to degraded performance. In this paper,
we tackle this efficiency bottleneck and propose the first instance-based
weight perturbation method: sharpness-aware minimization with dynamic
reweighting ({\delta}-SAM). {\delta}-SAM dynamically reweights perturbation
within each batch by estimated guardedness (i.e. unguarded instances are
up-weighted), serving as a better approximation to per-instance perturbation.
Experiments on various tasks demonstrate the effectiveness of {\delta}-SAM.

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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: chao-dyn
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: cmp-lg
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The categories of this paper are: patt-sol
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The categories of this paper are: solv-int
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The categories of this paper are: plasm-ph
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The categories of this paper are: math
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The categories of this paper are: dg-ga
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The categories of this paper are: funct-an
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The categories of this paper are: ao-sci
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The categories of this paper are: gr-qc
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The categories of this paper are: hep-th
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The categories of this paper are: q-fin