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Article overview
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One-Shot Learning in Discriminative Neural Networks | Jordan Burgess
; James Robert Lloyd
; Zoubin Ghahramani
; | Date: |
18 Jul 2017 | 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. | Source: | arXiv, 1707.5562 | Services: | Forum | Review | PDF | Favorites |
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