Understand and Improve Contrastive Learning Methods for Visual Representation: A Review [article]

Ran Liu
2021 arXiv   pre-print
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
more » ... ieved 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.
arXiv:2106.03259v1 fatcat:umiy7qxuinhdtjssqepl7i2xsy