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

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Authors Mehadi Hassen, Philip K. Chan
Journal/Conference Name Proceedings of the 2020 SIAM International Conference on Data Mining, SDM 2020
Paper Category
Paper Abstract Open set recognition problems exist in many domains. For example in security, new malware classes emerge regularly; therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known classes. In this paper we present a neural network based representation for addressing 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, resulting in statistically significant improvement when compared to other approaches on three datasets from two different domains.
Date of publication 2018
Code Programming Language Python
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