Unsupervised Learning to Overcome Catastrophic Forgetting in Neural Networks

Unsupervised Learning to Overcome Catastrophic Forgetting in Neural Networks

Continual learning is the ability to acquire a new task or knowledge without losing any previously collected information. Achieving continual learning in artificial intelligence (AI) is currently prevented by catastrophic forgetting, where training of a new task deletes all previously learned tasks....

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Bibliographic Details
Main Authors: Irene Munoz-Martin, Stefano Bianchi, Giacomo Pedretti, Octavian Melnic, Stefano Ambrogio, Daniele Ielmini
Format: Article
Language:English
Published: IEEE 2019-01-01
Series:IEEE Journal on Exploratory Solid-State Computational Devices and Circuits
Subjects:
Catastrophic forgetting
continual learning
convolutional neural network (CNN)
neuromorphic engineering
phase-change memory (PCM)
spike-timing-dependent plasticity (STDP)
Online Access:https://ieeexplore.ieee.org/document/8693665/

Internet

https://ieeexplore.ieee.org/document/8693665/

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