title: Overcoming catastrophic forgetting in neural networks.
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.
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom; kirkpatrick@google.com.
authors.forename: James
authors.initials: J
authors.lastname: Kirkpatrick
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Razvan
authors.initials: R
authors.lastname: Pascanu
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Neil
authors.initials: N
authors.lastname: Rabinowitz
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Joel
authors.initials: J
authors.lastname: Veness
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Desjardins
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Andrei A
authors.initials: AA
authors.lastname: Rusu
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Kieran
authors.initials: K
authors.lastname: Milan
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: John
authors.initials: J
authors.lastname: Quan
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Tiago
authors.initials: T
authors.lastname: Ramalho
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Agnieszka
authors.initials: A
authors.lastname: Grabska-Barwinska
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Demis
authors.initials: D
authors.lastname: Hassabis
authors.affiliation: Bioengineering Department, Imperial College London, London SW7 2AZ, United Kingdom.
authors.forename: Claudia
authors.initials: C
authors.lastname: Clopath
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Dharshan
authors.initials: D
authors.lastname: Kumaran
authors.affiliation: DeepMind, London EC4 5TW, United Kingdom.
authors.forename: Raia
authors.initials: R
authors.lastname: Hadsell
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003198:Computer Simulation; D006801:Humans; D007858:Learning; D008568:Memory; D011939:Mental Recall; D016571:Neural Networks, Computer
keywords: artificial intelligence; continual learning; deep learning; stability plasticity; synaptic consolidation