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arXiv · 1802.02950

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
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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 other state-of-the-art in lifelong learning without forgetting.

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Xialei Liu, Marc Masana, Luis Herranz, Joost Van de Weijer, Antonio M. Lopez, Andrew D. Bagdanov. 2018-02-08. Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting. https://arxiv.org/abs/1802.02950

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