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Better Self-training for Image Classification Through Self-supervision

Abstract
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.
Type
Conference Contribution
Type of thesis
Series
Citation
Date
2022-01-01
Publisher
SPRINGER INTERNATIONAL PUBLISHING AG
Degree
Supervisors
Rights
© 2022 Springer. This is an author’s accepted version of a conference paper published in the Lecture Notes in Computer Science book series (LNAI,volume 13151).