Semi-supervised learning in computer vision
Date
2022-12
Authors
Journal Title
Journal ISSN
Volume Title
Publisher
Stellenbosch : Stellenbosch University
Abstract
ENGLISH ABSTRACT: Deep learning models have proven to be successful at tasks such as image
classification. A major drawback of supervised learning is the need for large
labelled datasets to obtain good classification accuracy. This can be a bar rier to those in resource-constrained environments wanting to implement a
classification model in a previously unexplored field. Recent advancements
in unsupervised learning methods, such as contrastive learning, have made
it viable to perform representation learning without labels, which when com bined with supervised learning on relatively small labelled datasets can lead
to state-of-the-art performance on image classification tasks.
We study this technique, called semi-supervised learning, and provide an in vestigation into three semi-supervised learning frameworks. Our work starts
by discussing the implementations of the SimCLR, SimSiam and FixMatch
frameworks. We compare the results of each framework on the CIFAR-10 and
STL-10 datasets in label-scarce scenarios and show that: (1) all frameworks
outperform a purely supervised learning baseline when the number of labels is
reduced, (2) the improvement in performance of the frameworks over the su pervised baseline increases as the number of available labels is decreased and
(3) in most cases, the semi-supervised learning frameworks are able to match
or outperform the supervised baseline with 10% as many labels.
We also investigate the performance of the SimCLR and SimSiam framework
on class-imbalanced versions of the CIFAR-10 and STL-10 datasets, and find
that: (1) the improvements over the supervised learning baseline is less sub stantial than in the results with fewer overall, but balanced, class labels, and
(2) with basic oversampling implemented the results are significantly improved,
with the semi-supervised learning frameworks benefiting the most.
The results in this thesis indicate that unsupervised representation learning
can indeed lower the number of labelled images required for successful image
classification by a significant degree. We also show that each of the frameworks
considered in this work serves this function well.
"Geen opsomming beskikbaar"
"Geen opsomming beskikbaar"
Description
Thesis (MSc) -- Stellenbosch University, 2022.
Keywords
Supervised learning (Machine learning), Computer vision, Deep learning, Image classification, UCTD