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arXiv · 1805.07810

Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting

Hippolyt Ritter·Aleksandar Botev·David Barber
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Abstract

We introduce the Kronecker factored online Laplace approximation for overcoming catastrophic forgetting in neural networks. The method is grounded in a Bayesian online learning framework, where we recursively approximate the posterior after every task with a Gaussian, leading to a quadratic penalty on changes to the weights. The Laplace approximation requires calculating the Hessian around a mode, which is typically intractable for modern architectures. In order to make our method scalable, we leverage recent block-diagonal Kronecker factored approximations to the curvature. Our algorithm achieves over 90% test accuracy across a sequence of 50 instantiations of the permuted MNIST dataset, substantially outperforming related methods for overcoming catastrophic forgetting.

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Hippolyt Ritter, Aleksandar Botev, David Barber. 2018-05-20. Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting. https://arxiv.org/abs/1805.07810

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