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When learning new tasks in a sequential manner, deep neural networks tend to forget tasks that they previously learned, a phenomenon called catastrophic forgetting.
Catastrophic forgetting in connectionist networks
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
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Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
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James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
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Memory aware synapses: Learning what (not) to forget
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Zhiyuan Li, Ruosong Wang, Dingli Yu, Simon S Du, Wei Hu, Ruslan Salakhutdinov, and Sanjeev Arora · 2019
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Class-incremental learning: survey and performance evaluation on image classification
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Can we characterize tasks without labels or features?
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Class-incremental learning by knowledge distillation with adaptive feature consolidation
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