Overcoming catastrophic forgetting in neural networks

James Kirkpatrick1, Razvan Pascanu1, Neil C. Rabinowitz1, Joel Veness1, Guillaume Desjardins1, Andrei A. Rusu1, Kieran Milan1, John Quan1, Tiago Ramalho1, Agnieszka Grabska‐Barwińska1, Demis Hassabis1, Claudia Clopath2, Dharshan Kumaran1, Raia Hadsell1
1DeepMind, London EC4 5TW, United Kingdom;
2Bioengineering Department, Imperial College London, London SW7 2AZ, United Kingdom

Tóm tắt

Significance Deep neural networks are currently the most successful machine-learning technique for solving a variety of tasks, including language translation, image classification, and image generation. One weakness of such models is that, unlike humans, they are unable to learn multiple tasks sequentially. In this work we propose a practical solution to train such models sequentially by protecting the weights important for previous tasks. This approach, inspired by synaptic consolidation in neuroscience, enables state of the art results on multiple reinforcement learning problems experienced sequentially.

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