  Self-supervised learning (SSL) is a powerful tool in machine learning, but
understanding the learned representations and their underlying mechanisms
remains a challenge. This paper presents an in-depth empirical analysis of
SSL-trained representations, encompassing diverse models, architectures, and
hyperparameters. Our study reveals an intriguing aspect of the SSL training
process: it inherently facilitates the clustering of samples with respect to
semantic labels, which is surprisingly driven by the SSL objective's
regularization term. This clustering process not only enhances downstream
classification but also compresses the data information. Furthermore, we
establish that SSL-trained representations align more closely with semantic
classes rather than random classes. Remarkably, we show that learned
representations align with semantic classes across various hierarchical levels,
and this alignment increases during training and when moving deeper into the
network. Our findings provide valuable insights into SSL's representation
learning mechanisms and their impact on performance across different sets of
classes.
