  In the past few years, we have witnessed remarkable breakthroughs in
self-supervised representation learning. Despite the success and adoption of
representations learned through this paradigm, much is yet to be understood
about how different training methods and datasets influence performance on
downstream tasks. In this paper, we analyze contrastive approaches as one of
the most successful and popular variants of self-supervised representation
learning. We perform this analysis from the perspective of the training
algorithms, pre-training datasets and end tasks. We examine over 700 training
experiments including 30 encoders, 4 pre-training datasets and 20 diverse
downstream tasks. Our experiments address various questions regarding the
performance of self-supervised models compared to their supervised
counterparts, current benchmarks used for evaluation, and the effect of the
pre-training data on end task performance. We hope the insights and empirical
evidence provided by this work will help future research in learning better
visual representations.
