Learning a Neural-network-based Representation for Open Set Recognition
Mehadi Hassen, Philip K. Chan
Abstract
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.
Topics & Concepts
Open setRepresentation (politics)Computer scienceSet (abstract data type)Class (philosophy)Artificial intelligenceArtificial neural networkPattern recognition (psychology)Machine learningMathematicsDiscrete mathematicsProgramming languagePoliticsLawPolitical scienceNeural Networks and Applications