Learning a Neural-network-based Representation for Open Set Recognition

In this paper, we present a neural network based representation for the Open Set Recognition problem. In this representation instances from the same class are close to each other while instances from different classes are further apart. When used for Open Set Recognition tasks, evaluated on three datasets from two different domains, the proposed approach results in a statistically significant improvement compared to other approaches.