Deep Open Set Recognition Using Dynamic Intra-class Splitting

Abstract

This paper provides a generic deep learning method to solve open set recognition problems. In open set recognition, only samples of a limited number of known classes are given for training. During inference, an open set recognizer must not only correctly classify samples from known classes, but also reject samples from unknown classes. Due to these specific requirements, conventional deep learning models that assume a closed set environment cannot be used. Therefore, special open set approaches were taken, including variants of support vector machines and generation-based state-of-the-art methods which model unknown classes by generated samples. In contrast, our proposed method models unknown classes by atypical subsets of training samples. The subsets are obtained through intra-class splitting (ICS). Based on a recently proposed two-stage algorithm using ICS, we propose a one-stage method based on alternating between ICS and the training of a deep neural network. Finally, several experiments were conducted to compare our proposed method with conventional and other state-of-the-art methods. The proposed method based on dynamic ICS showed a comparable or better performance than all considered existing methods regarding balanced accuracy.