Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Sharpness-aware Minimization for Improved Training of Neural
  Networks   Overparametrized Deep Neural Networks (DNNs) often achieve astounding
performances, but may potentially result in severe generalization error.
Recently, the relation between the sharpness of the loss landscape and the
generalization error has been established by Foret et al. (2020), in which the
Sharpness Aware Minimizer (SAM) was proposed to mitigate the degradation of the
generalization. Unfortunately, SAM s computational cost is roughly double that
of base optimizers, such as Stochastic Gradient Descent (SGD). This paper thus
proposes Efficient Sharpness Aware Minimizer (ESAM), which boosts SAM s
efficiency at no cost to its generalization performance. ESAM includes two
novel and efficient training strategies-StochasticWeight Perturbation and
Sharpness-Sensitive Data Selection. In the former, the sharpness measure is
approximated by perturbing a stochastically chosen set of weights in each
iteration; in the latter, the SAM loss is optimized using only a judiciously
selected subset of data that is sensitive to the sharpness. We provide
theoretical explanations as to why these strategies perform well. We also show,
via extensive experiments on the CIFAR and ImageNet datasets, that ESAM
enhances the efficiency over SAM from requiring 100% extra computations to 40%
vis-a-vis base optimizers, while test accuracies are preserved or even
improved.

17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
30
The categories of this paper are: q-alg
10
The categories of this paper are: q-bio
19
The categories of this paper are: eess
0
The categories of this paper are: hep-ph
33
The categories of this paper are: plasm-ph
35
The categories of this paper are: mtrl-th
37
The categories of this paper are: supr-con
25
The categories of this paper are: patt-sol
27
The categories of this paper are: comp-gas
16
The categories of this paper are: stat
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
13
The categories of this paper are: quant-ph
2
The categories of this paper are: math