🤖 AI Summary
This work addresses the challenge of catastrophic forgetting in artificial neural networks during continual learning. Inspired by biological sleep mechanisms, the authors propose an unsupervised, sleep-like replay phase that consolidates all previously acquired knowledge in a unified manner after sequential task learning, without requiring immediate intervention during training. Departing from conventional continual learning paradigms, this approach demonstrates for the first time that a single replay session conducted after the entire task sequence can effectively mitigate forgetting. The study further reveals that information from prior tasks undergoes gradual decay rather than abrupt overwriting during new learning. Experimental results show that this strategy substantially recovers performance across all previously learned tasks, offering a novel and biologically inspired perspective on memory consolidation in continual learning systems.
📝 Abstract
One of the critical limitations of artificial neural networks is their lack of ability to continually learn: training on new tasks often leads to interference and forgetting of the previous ones. While several algorithms have been proposed to protect old memories from interference, they are typically applied during or immediately after each new episode of training. In contrast, humans and animals can learn continuously, acquiring multiple new memories during active learning before consolidating all of them into long-term storage. Here we show that multiple new tasks can be trained sequentially before an unsupervised sleep-like replay phase is applied to partially restore performance across all previously learned tasks. Our study further suggests that task-specific information remains resilient to new training but decays gradually as network is trained on new tasks. These findings point to novel principles for developing a broad range of continual learning AI solutions.