Towards Combined Open Set Recognition and Out-of-Distribution Detection for Fine-grained Classification

We analyze the two very similar problems of Out-of-Distribution (OOD) Detection and Open Set Recognition (OSR) in the context of fine-grained classification. Both problems are about detecting object classes that a classifier was not trained on, but while the former aims to reject invalid inputs, the latter aims to detect valid but unknown classes. Previous works on OOD detection and OSR methods are evaluated mostly on very simple datasets or datasets with large inter-class variance and perform poorly in the fine-grained setting. In our experiments, we show that object detection works well to recognize invalid inputs and techniques from the field of fine-grained classification, like individual part detection or zooming into discriminative local regions, are helpful for fine-grained OSR.