  Traditional supervised learning methods are hitting a bottleneck because of
their dependency on expensive manually labeled data and their weaknesses such
as limited generalization ability and vulnerability to adversarial attacks. A
promising alternative, self-supervised learning, as a type of unsupervised
learning, has gained popularity because of its potential to learn effective
data representations without manual labeling. Among self-supervised learning
algorithms, contrastive learning has achieved state-of-the-art performance in
several fields of research. This literature review aims to provide an
up-to-date analysis of the efforts of researchers to understand the key
components and the limitations of self-supervised learning.
