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Proc Natl Acad Sci U S A. 2017 Mar 28;114(13):3521-3526. doi: 10.1073/pnas.1611835114. Epub 2017 Mar 14.

Overcoming catastrophic forgetting in neural networks.

Author information

1
DeepMind, London EC4 5TW, United Kingdom; kirkpatrick@google.com.
2
DeepMind, London EC4 5TW, United Kingdom.
3
Bioengineering Department, Imperial College London, London SW7 2AZ, United Kingdom.

Abstract

The ability to learn tasks in a sequential fashion is crucial to the development of artificial intelligence. Until now neural networks have not been capable of this and it has been widely thought that catastrophic forgetting is an inevitable feature of connectionist models. We show that it is possible to overcome this limitation and train networks that can maintain expertise on tasks that they have not experienced for a long time. Our approach remembers old tasks by selectively slowing down learning on the weights important for those tasks. We demonstrate our approach is scalable and effective by solving a set of classification tasks based on a hand-written digit dataset and by learning several Atari 2600 games sequentially.

KEYWORDS:

artificial intelligence; continual learning; deep learning; stability plasticity; synaptic consolidation

PMID:
28292907
PMCID:
PMC5380101
DOI:
10.1073/pnas.1611835114
[Indexed for MEDLINE]
Free PMC Article

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