Know Your Self-supervised Learning: A Survey on Image-based Generative
  and Discriminative Training
Utku Ozbulak Hyun Jung Lee Beril Boga Esla Timothy Anzaku Homin Park Arnout Van Messem Wesley De Neve Joris Vankerschaver
Although supervised learning has been highly successful in improving the
state-of-the-art in the domain of image-based computer vision in the past, the
margin of improvement has diminished significantly in recent years, indicating
that a plateau is in sight. Meanwhile, the use of self-supervised learning
(SSL) for the purpose of natural language processing (NLP) has seen tremendous
successes during the past couple of years, with this new learning paradigm
yielding powerful language models. Inspired by the excellent results obtained
in the field of NLP, self-supervised methods that rely on clustering,
contrastive learning, distillation, and information-maximization, which all
fall under the banner of discriminative SSL, have experienced a swift uptake in
the area of computer vision. Shortly afterwards, generative SSL frameworks that
are mostly based on masked image modeling, complemented and surpassed the
results obtained with discriminative SSL. Consequently, within a span of three
years, over $100$ unique general-purpose frameworks for generative and
discriminative SSL, with a focus on imaging, were proposed. In this survey, we
review a plethora of research efforts conducted on image-oriented SSL,
providing a historic view and paying attention to best practices as well as
useful software packages. While doing so, we discuss pretext tasks for
image-based SSL, as well as techniques that are commonly used in image-based
SSL. Lastly, to aid researchers who aim at contributing to image-focused SSL,
we outline a number of promising research directions.