Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting

Xialei Liu, Marc Masana, Luis Herranz, Joost van de Weijer, Antonio M. Lopez, Andrew D. Bagdanov

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

In this paper we propose an approach to avoiding catastrophic forgetting in sequential task learning scenarios. Our technique is based on a network reparameterization that approximately diagonalizes the Fisher Information Matrix of the network parameters. This reparameterization takes the form of a factorized rotation of parameter space which, when used in conjunction with Elastic Weight Consolidation (which assumes a diagonal Fisher Information Matrix), leads to significantly better performance on lifelong learning of sequential tasks. Experimental results on the MNIST, CIFAR-100, CUB-200 and Stanford-40 datasets demonstrate that we significantly improve the results of standard elastic weight consolidation, and that we obtain competitive results when compared to the state-of-the-art in lifelong learning without forgetting.
Original languageEnglish
Title of host publicationProceedings - International Conference on Pattern Recognition
DOIs
Publication statusPublished - 2018
Externally publishedYes

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