FBCC enables clustering models to learn from streaming unlabeled data sequentially without forgetting previous clusters or storing old data, using a teacher-student distillation approach instead of memory replay.
This paper tackles unsupervised continual learning for clustering tasks, where models must learn from sequential unlabeled data without storing past examples.