Patch-Based Contrastive Learning and Memory Consolidation for Efficient Online Unsupervised Continual Learning

PCMC builds a compositional understanding of data by identifying and clustering patch-level features, using an encoder trained via patch-based contrastive learning. It incorporates new data into its distribution while avoiding catastrophic forgetting, and consolidates memory examples during "sleep" periods.