A Survey on Self-Supervised Representation Learning
Tobias Uelwer Jan Robine Stefan Sylvius Wagner Marc Höftmann Eric Upschulte Sebastian Konietzny Maike Behrendt Stefan Harmeling
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.