Synaptic metaplasticity in binarized neural networks
2021-01-19Code Available1· sign in to hype
Axel Laborieux, Maxence Ernoult, Tifenn Hirtzlin, Damien Querlioz
Code Available — Be the first to reproduce this paper.
ReproduceCode
- github.com/Laborieux-Axel/SynapticMetaplasticityBNNOfficialpytorch★ 21
Abstract
Unlike the brain, artificial neural networks, including state-of-the-art deep neural networks for computer vision, are subject to "catastrophic forgetting": they rapidly forget the previous task when trained on a new one. Neuroscience suggests that biological synapses avoid this issue through the process of synaptic consolidation and metaplasticity: the plasticity itself changes upon repeated synaptic events. In this work, we show that this concept of metaplasticity can be transferred to a particular type of deep neural networks, binarized neural networks, to reduce catastrophic forgetting.