  Unsupervised Continual Learning (UCL) is a burgeoning field in machine
learning, focusing on enabling neural networks to sequentially learn tasks
without explicit label information. Catastrophic Forgetting (CF), where models
forget previously learned tasks upon learning new ones, poses a significant
challenge in continual learning, especially in UCL, where labeled information
of data is not accessible. CF mitigation strategies, such as knowledge
distillation and replay buffers, often face memory inefficiency and privacy
issues. Although current research in UCL has endeavored to refine data
representations and address CF in streaming data contexts, there is a
noticeable lack of algorithms specifically designed for unsupervised
clustering. To fill this gap, in this paper, we introduce the concept of
Unsupervised Continual Clustering (UCC). We propose Forward-Backward Knowledge
Distillation for unsupervised Continual Clustering (FBCC) to counteract CF
within the context of UCC. FBCC employs a single continual learner (the
``teacher'') with a cluster projector, along with multiple student models, to
address the CF issue. The proposed method consists of two phases: Forward
Knowledge Distillation, where the teacher learns new clusters while retaining
knowledge from previous tasks with guidance from specialized student models,
and Backward Knowledge Distillation, where a student model mimics the teacher's
behavior to retain task-specific knowledge, aiding the teacher in subsequent
tasks. FBCC marks a pioneering approach to UCC, demonstrating enhanced
performance and memory efficiency in clustering across various tasks,
outperforming the application of clustering algorithms to the latent space of
state-of-the-art UCL algorithms.
