  Self-supervised representation learning in computer vision relies heavily on
hand-crafted image transformations to learn meaningful and invariant features.
However few extensive explorations of the impact of transformation design have
been conducted in the literature. In particular, the dependence of downstream
performances to transformation design has been established, but not studied in
depth. In this work, we explore this relationship, its impact on a domain other
than natural images, and show that designing the transformations can be viewed
as a form of supervision. First, we demonstrate that not only do
transformations have an effect on downstream performance and relevance of
clustering, but also that each category in a supervised dataset can be impacted
in a different way. Following this, we explore the impact of transformation
design on microscopy images, a domain where the difference between classes is
more subtle and fuzzy than in natural images. In this case, we observe a
greater impact on downstream tasks performances. Finally, we demonstrate that
transformation design can be leveraged as a form of supervision, as careful
selection of these by a domain expert can lead to a drastic increase in
performance on a given downstream task.
