Self-recovery of memory via generative replay
This addresses the issue of forgetting in continual learning for AI systems, offering a neurally-plausible and memory-efficient solution.
The paper tackles the problem of artificial neural networks lacking the brain's ability to autonomously reorganize memories during offline periods, proposing a novel architecture that enables generative replay to self-recover memories, achieving state-of-the-art performance on continual learning benchmarks.
A remarkable capacity of the brain is its ability to autonomously reorganize memories during offline periods. Memory replay, a mechanism hypothesized to underlie biological offline learning, has inspired offline methods for reducing forgetting in artificial neural networks in continual learning settings. A memory-efficient and neurally-plausible method is generative replay, which achieves state of the art performance on continual learning benchmarks. However, unlike the brain, standard generative replay does not self-reorganize memories when trained offline on its own replay samples. We propose a novel architecture that augments generative replay with an adaptive, brain-like capacity to autonomously recover memories. We demonstrate this capacity of the architecture across several continual learning tasks and environments.