A Survey on Self-Supervised Representation Learning
It provides a comprehensive overview for researchers and practitioners entering the field of representation learning, but it is incremental as it synthesizes existing work.
This survey paper reviews self-supervised representation learning methods for images, comparing their approaches and summarizing experimental results to show that their quality approaches supervised learning without requiring labeled data.
Learning meaningful representations is at the heart of many tasks in the field of modern machine learning. Recently, a lot of methods were introduced that allow learning of image representations without supervision. These representations can then be used in downstream tasks like classification or object detection. The quality of these representations is close to supervised learning, while no labeled images are needed. This survey paper provides a comprehensive review of these methods in a unified notation, points out similarities and differences of these methods, and proposes a taxonomy which sets these methods in relation to each other. Furthermore, our survey summarizes the most-recent experimental results reported in the literature in form of a meta-study. Our survey is intended as a starting point for researchers and practitioners who want to dive into the field of representation learning.