2021/02/24 by Hakan Çevıkalp, Hakan Cevikalp, Bedirhan Uzun +7
Computer Science · Engineering · #Computer Vision and Pattern Recognition (cs.CV) #Domain Adaptation and Few-Shot Learning #FOS: Computer and information sciences #Image Processing Techniques and Applications #Remote-Sensing Image Classification #cs.CV
paper · pdf · doi:10.48550/arxiv.2102.12570
arxiv created 2021/02/24 · openalex publication_date 2021/02/24 · arxiv updated 2021/02/26 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
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.