I. Introduction
Object classification traditionally assumes complete knowledge about all classes the classifier encounters during inference [1]–[3]. The availability of out-of-class training data allows the classification networks to learn discriminative features that separates each class from the rest. Recent classification networks have exploited this property to learn representations that result in superior classification performance on various datasets.