  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.
