Online Continual Learning under Extreme Memory Constraints
This addresses the challenge of catastrophic forgetting in continual learning for AI systems with limited memory, though it is incremental as it builds on existing distillation techniques.
The paper tackles the problem of online continual learning under strict memory constraints by proposing Batch-level Distillation (BLD), which achieves comparable accuracy to prior methods while using less memory, as demonstrated on three benchmarks.
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.