One-Shot Learning in Discriminative Neural Networks

By Jordan Burgess, James Robert Lloyd, Zoubin Ghahramani. 2017

11 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Discriminative k-shot learning using probabilistic models (2017, methodology): “It subsumes two existing approaches in this vein [5, 6], and is motivated by similar ideas from multi-task learning [7].”