  To improve performance in visual feature representation from photos or videos
for practical applications, we generally require large-scale human-annotated
labeled data while training deep neural networks. However, the cost of
gathering and annotating human-annotated labeled data is expensive. Given that
there is a lot of unlabeled data in the actual world, it is possible to
introduce self-defined pseudo labels as supervisions to prevent this issue.
Self-supervised learning, specifically contrastive learning, is a subset of
unsupervised learning methods that has grown popular in computer vision,
natural language processing, and other domains. The purpose of contrastive
learning is to embed augmented samples from the same sample near to each other
while pushing away those that are not. In the following sections, we will
introduce the regular formulation among different learnings. In the next
sections, we will discuss the regular formulation of various learnings.
Furthermore, we offer some strategies from contrastive learning that have
recently been published and are focused on pretext tasks for visual
representation.
