Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Better Self-training for Image Classification through Self-supervision   Self-training is a simple semi-supervised learning approach: Unlabelled
examples that attract high-confidence predictions are labelled with their
predictions and added to the training set, with this process being repeated
multiple times. Recently, self-supervision -- learning without manual
supervision by solving an automatically-generated pretext task -- has gained
prominence in deep learning. This paper investigates three different ways of
incorporating self-supervision into self-training to improve accuracy in image
classification: self-supervision as pretraining only, self-supervision
performed exclusively in the first iteration of self-training, and
self-supervision added to every iteration of self-training. Empirical results
on the SVHN, CIFAR-10, and PlantVillage datasets, using both training from
scratch, and Imagenet-pretrained weights, show that applying self-supervision
only in the first iteration of self-training can greatly improve accuracy, for
a modest increase in computation time.

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The categories of this paper are: supr-con
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
8
The categories of this paper are: hep-ex
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The categories of this paper are: dg-ga
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The categories of this paper are: cmp-lg
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The categories of this paper are: stat
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The categories of this paper are: patt-sol
33
The categories of this paper are: plasm-ph
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The categories of this paper are: funct-an
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The categories of this paper are: hep-lat
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The categories of this paper are: solv-int
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The categories of this paper are: gr-qc
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The categories of this paper are: quant-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: nlin