Poster
in
Workshop: Memory in Artificial and Real Intelligence (MemARI)

Self-recovery of memory via generative replay

Zhenglong Zhou · Geshi Yeung · Anna Schapiro

Keywords: [ continual learning ] [ offline self-reorganization ] [ memory recovery ] [ generative replay ]


Abstract:

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 a brain-like capacity to autonomously recover memories. We demonstrate this capacity of the architecture across several continual learning tasks and environments.

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