Spatial Location Constraint Prototype Loss for Open Set Recognition

One of the challenges in pattern recognition is open set recognition. Compared with closed recognition, needs to reduce not only empirical risk, but also space and reduction these two risks corresponds classifying known classes identifying unknown respectively. How risk key This paper explores origin by analyzing distribution features. On this basis, spatial location constraint prototype loss function proposed simultaneously. Extensive experiments on multiple benchmark datasets many visualization results indicate that our methods superior most existing approaches.