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Overcoming catastrophic forgetting in neural networks

2017; National Academy of Sciences; Volume: 114; Issue: 13 Linguagem: Inglês

10.1073/pnas.1611835114

1091-6490

James Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska‐Barwińska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, Raia Hadsell,

Domain Adaptation and Few-Shot Learning

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.