Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition
Abstract
In this paper, we propose a new deep neural network classifier that simultaneously maximizes the inter-class separation and minimizes the intra-class variation by using the polyhedral conic classification function. The proposed method has one loss term that allows the margin maximization to maximize the inter-class separation and another loss term that controls the compactness of the class acceptance regions. Our proposed method has a nice geometric interpretation using polyhedral conic function geometry. We tested the proposed method on various visual classification problems including closed/open set recognition and anomaly detection. The experimental results show that the proposed method typically outperforms other state-of-the art methods, and becomes a better choice compared to other tested methods especially for open set recognition type problems.
Cite
@article{arxiv.2102.12570,
title = {Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition},
author = {Hakan Cevikalp and Bedirhan Uzun and Okan Köpüklü and Gurkan Ozturk},
journal= {arXiv preprint arXiv:2102.12570},
year = {2021}
}