Overcoming Catastrophic Interference using Conceptor-Aided BackpropagationDownload PDF

15 Feb 2018, 21:29 (modified: 10 Feb 2022, 11:36)ICLR 2018 Conference Blind SubmissionReaders: Everyone
Keywords: Catastrophic Interference, Conceptor, Backpropagation, Continual Learning, Lifelong Learning
TL;DR: We propose a variant of the backpropagation algorithm, in which gradients are shielded by conceptors against degradation of previously learned tasks.
Abstract: Catastrophic interference has been a major roadblock in the research of continual learning. Here we propose a variant of the back-propagation algorithm, "Conceptor-Aided Backprop" (CAB), in which gradients are shielded by conceptors against degradation of previously learned tasks. Conceptors have their origin in reservoir computing, where they have been previously shown to overcome catastrophic forgetting. CAB extends these results to deep feedforward networks. On the disjoint and permuted MNIST tasks, CAB outperforms two other methods for coping with catastrophic interference that have recently been proposed.
Data: [Permuted MNIST](https://paperswithcode.com/dataset/permuted-mnist)
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