How do self-supervised clustering objectives integrate into deep architectures?

They combine clustering assignments with representation learning losses to jointly optimize structure and embeddings.

Self-supervised clustering integrates clustering signals directly into neural network training. Methods like SwAV use online clustering assignments without explicit labels, enforcing consistency between augmented views. This avoids trivial solutions by balancing entropy and prototype assignments. The architecture learns both cluster structure and embedding space simultaneously.