  This paper proposes a method to use deep neural networks as end-to-end
open-set classifiers. It is based on intra-class data splitting. In open-set
recognition, only samples from a limited number of known classes are available
for training. During inference, an open-set classifier must reject samples from
unknown classes while correctly classifying samples from known classes. The
proposed method splits given data into typical and atypical normal subsets by
using a closed-set classifier. This enables to model the abnormal classes by
atypical normal samples. Accordingly, the open-set recognition problem is
reformulated into a traditional classification problem. In addition, a
closed-set regularization is proposed to guarantee a high closed-set
classification performance. Intensive experiments on five well-known image
datasets showed the effectiveness of the proposed method which outperformed the
baselines and achieved a distinct improvement over the state-of-the-art
methods.
