  Deep supervised learning has achieved great success in the last decade.
However, its deficiencies of dependence on manual labels and vulnerability to
attacks have driven people to explore a better solution. As an alternative,
self-supervised learning attracts many researchers for its soaring performance
on representation learning in the last several years. Self-supervised
representation learning leverages input data itself as supervision and benefits
almost all types of downstream tasks. In this survey, we take a look into new
self-supervised learning methods for representation in computer vision, natural
language processing, and graph learning. We comprehensively review the existing
empirical methods and summarize them into three main categories according to
their objectives: generative, contrastive, and generative-contrastive
(adversarial). We further investigate related theoretical analysis work to
provide deeper thoughts on how self-supervised learning works. Finally, we
briefly discuss open problems and future directions for self-supervised
learning. An outline slide for the survey is provided.
