  Joint-Embedding Self Supervised Learning (JE-SSL) has seen a rapid
development, with the emergence of many method variations but only few
principled guidelines that would help practitioners to successfully deploy
them. The main reason for that pitfall comes from JE-SSL's core principle of
not employing any input reconstruction therefore lacking visual cues of
unsuccessful training. Adding non informative loss values to that, it becomes
difficult to deploy SSL on a new dataset for which no labels can help to judge
the quality of the learned representation. In this study, we develop a simple
unsupervised criterion that is indicative of the quality of the learned JE-SSL
representations: their effective rank. Albeit simple and computationally
friendly, this method -- coined RankMe -- allows one to assess the performance
of JE-SSL representations, even on different downstream datasets, without
requiring any labels. A further benefit of RankMe is that it does not have any
training or hyper-parameters to tune. Through thorough empirical experiments
involving hundreds of training episodes, we demonstrate how RankMe can be used
for hyperparameter selection with nearly no reduction in final performance
compared to the current selection method that involve a dataset's labels. We
hope that RankMe will facilitate the deployment of JE-SSL towards domains that
do not have the opportunity to rely on labels for representations' quality
assessment.
