A lecture that discusses continual learning and catastrophic forgetting in deep neural networks. We discuss the context, methods for evaluating algorithms, and algorithms based on regularization, dynamic architectures, and Complementary Learning Systems. Specifically, we discuss data permutation tasks, incremental task learning, multimodal learning, the Learning without Forgetting algorithm, Elastic Weight Consolidation, Progressive Neural Networks, and Generative replay.
This lecture is from Northeastern University's CS 7150 Summer 2020 class on Deep Learning, taught by Paul Hand.
The notes are available at: [ Ссылка ]
References:
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Shin et al. 2017:
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