  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.
