Synaptic metaplasticity in binarized neural networks

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 thro...

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Veröffentlicht in:arXiv.org
Hauptverfasser: Laborieux, Axel, Ernoult, Maxence, Hirtzlin, Tifenn, Querlioz, Damien
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Sprache:Englisch
Veröffentlicht: Ithaca Cornell University Library, arXiv.org 19.01.2021
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ISSN:2331-8422
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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.
AbstractList 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.
Author Ernoult, Maxence
Querlioz, Damien
Laborieux, Axel
Hirtzlin, Tifenn
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Title Synaptic metaplasticity in binarized neural networks
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