  Unsupervised continual learning remains a relatively uncharted territory in
the existing literature because the vast majority of existing works call for
unlimited access of ground truth incurring expensive labelling cost. Another
issue lies in the problem of task boundaries and task IDs which must be known
for model's updates or model's predictions hindering feasibility for real-time
deployment. Knowledge Retention in Self-Adaptive Deep Continual Learner,
(KIERA), is proposed in this paper. KIERA is developed from the notion of
flexible deep clustering approach possessing an elastic network structure to
cope with changing environments in the timely manner. The centroid-based
experience replay is put forward to overcome the catastrophic forgetting
problem. KIERA does not exploit any labelled samples for model updates while
featuring a task-agnostic merit. The advantage of KIERA has been numerically
validated in popular continual learning problems where it shows highly
competitive performance compared to state-of-the art approaches. Our
implementation is available in
\textit{\url{https://github.com/ContinualAL/KIERA}}.
