Abstract
This work addresses the novel problem of one-shot oneclass classification. The goal is to estimate a classification
decision boundary for a novel class based on a single image
example. Our method exploits transfer learning to model
the transformation from a representation of the input, extracted by a Convolutional Neural Network, to a classification decision boundary. We use a deep neural network to
learn this transformation from a large labelled dataset of
images and their associated class decision boundaries generated from ImageNet, and then apply the learned decision
boundary to classify subsequent query images. We tested
our approach on several benchmark datasets and signifi-
cantly outperformed the baseline methods