Regularising Deep Networks with Deep Generative Models

Matthew Willetts; Alexander Camuto; Stephen Roberts; Chris Holmes

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arXiv · 1909.11507

Regularising Deep Networks with Deep Generative Models

Matthew Willetts·Alexander Camuto·Stephen Roberts·Chris Holmes

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Abstract

We develop a new method for regularising neural networks. We learn a probability distribution over the activations of all layers of the model and then insert imputed values into the network during training. We obtain a posterior for an arbitrary subset of activations conditioned on the remainder. This is a generalisation of data augmentation to the hidden layers of a network, and a form of data-aware dropout. We demonstrate that our training method leads to higher test accuracy and lower test-set cross-entropy for neural networks trained on CIFAR-10 and SVHN compared to standard regularisation baselines: our approach leads to networks with better calibrated uncertainty over the class posteriors all the while delivering greater test-set accuracy.

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Matthew Willetts, Alexander Camuto, Stephen Roberts, Chris Holmes. 2019-09-25. Regularising Deep Networks with Deep Generative Models. https://arxiv.org/abs/1909.11507

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