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arXiv · 1707.05562

One-Shot Learning in Discriminative Neural Networks

Jordan Burgess·James Robert Lloyd·Zoubin Ghahramani
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Abstract

We consider the task of one-shot learning of visual categories. In this paper we explore a Bayesian procedure for updating a pretrained convnet to classify a novel image category for which data is limited. We decompose this convnet into a fixed feature extractor and softmax classifier. We assume that the target weights for the new task come from the same distribution as the pretrained softmax weights, which we model as a multivariate Gaussian. By using this as a prior for the new weights, we demonstrate competitive performance with state-of-the-art methods whilst also being consistent with 'normal' methods for training deep networks on large data.

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Jordan Burgess, James Robert Lloyd, Zoubin Ghahramani. 2017-07-18. One-Shot Learning in Discriminative Neural Networks. https://arxiv.org/abs/1707.05562

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