  Continual Learning (CL) aims to develop agents emulating the human ability to
sequentially learn new tasks while being able to retain knowledge obtained from
past experiences. In this paper, we introduce the novel problem of
Memory-Constrained Online Continual Learning (MC-OCL) which imposes strict
constraints on the memory overhead that a possible algorithm can use to avoid
catastrophic forgetting. As most, if not all, previous CL methods violate these
constraints, we propose an algorithmic solution to MC-OCL: Batch-level
Distillation (BLD), a regularization-based CL approach, which effectively
balances stability and plasticity in order to learn from data streams, while
preserving the ability to solve old tasks through distillation. Our extensive
experimental evaluation, conducted on three publicly available benchmarks,
empirically demonstrates that our approach successfully addresses the MC-OCL
problem and achieves comparable accuracy to prior distillation methods
requiring higher memory overhead.
