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.
@article{arxiv.2101.07592,
title = {Synaptic metaplasticity in binarized neural networks},
author = {Axel Laborieux and Maxence Ernoult and Tifenn Hirtzlin and Damien Querlioz},
journal= {arXiv preprint arXiv:2101.07592},
year = {2021}
}