Open-Set Recognition Using Intra-Class Splitting
It addresses the problem of rejecting unknown classes during inference for applications like image recognition, but appears incremental as it builds on existing open-set recognition frameworks.
The paper tackled open-set recognition by reformulating it as a traditional classification problem using intra-class data splitting, achieving distinct improvements over state-of-the-art methods on five image datasets.
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.