  Deep learning has been the subject of growing interest in recent years.
Specifically, a specific type called Multimodal learning has shown great
promise for solving a wide range of problems in domains such as language,
vision, audio, etc. One promising research direction to improve this further
has been learning rich and robust low-dimensional data representation of the
high-dimensional world with the help of large-scale datasets present on the
internet. Because of its potential to avoid the cost of annotating large-scale
datasets, self-supervised learning has been the de facto standard for this task
in recent years. This paper summarizes some of the landmark research papers
that are directly or indirectly responsible to build the foundation of
multimodal self-supervised learning of representation today. The paper goes
over the development of representation learning over the last few years for
each modality and how they were combined to get a multimodal agent later.
