  The recent emergence of Self-Supervised Learning (SSL) as a fundamental
paradigm for learning image representations has, and continues to, demonstrate
high empirical success in a variety of tasks. However, most SSL approaches fail
to learn embeddings that capture hierarchical semantic concepts that are
separable and interpretable. In this work, we aim to learn highly separable
semantic hierarchical representations by stacking Joint Embedding Architectures
(JEA) where higher-level JEAs are input with representations of lower-level
JEA. This results in a representation space that exhibits distinct
sub-categories of semantic concepts (e.g., model and colour of vehicles) in
higher-level JEAs. We empirically show that representations from stacked JEA
perform on a similar level as traditional JEA with comparative parameter counts
and visualise the representation spaces to validate the semantic hierarchies.
