Self-recovery of memory via generative replay

• URL: http://arxiv.org/abs/2301.06030v1
• Date: Sun, 15 Jan 2023 07:28:14 GMT
• Title: Self-recovery of memory via generative replay
• Authors: Zhenglong Zhou, Geshi Yeung, Anna C. Schapiro
• Abstract summary: 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.
• Score: 0.8594140167290099
• License: http://creativecommons.org/licenses/by/4.0/
• 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 an adaptive, 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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