  Traditional machine learning follows a close-set assumption that the training
and test set share the same label space. While in many practical scenarios, it
is inevitable that some test samples belong to unknown classes (open-set). To
fix this issue, Open-Set Recognition (OSR), whose goal is to make correct
predictions on both close-set samples and open-set samples, has attracted
rising attention. In this direction, the vast majority of literature focuses on
the pattern of open-set samples. However, how to evaluate model performance in
this challenging task is still unsolved. In this paper, a systematic analysis
reveals that most existing metrics are essentially inconsistent with the
aforementioned goal of OSR: (1) For metrics extended from close-set
classification, such as Open-set F-score, Youden's index, and Normalized
Accuracy, a poor open-set prediction can escape from a low performance score
with a superior close-set prediction. (2) Novelty detection AUC, which measures
the ranking performance between close-set and open-set samples, ignores the
close-set performance. To fix these issues, we propose a novel metric named
OpenAUC. Compared with existing metrics, OpenAUC enjoys a concise pairwise
formulation that evaluates open-set performance and close-set performance in a
coupling manner. Further analysis shows that OpenAUC is free from the
aforementioned inconsistency properties. Finally, an end-to-end learning method
is proposed to minimize the OpenAUC risk, and the experimental results on
popular benchmark datasets speak to its effectiveness. Project Page:
https://github.com/wang22ti/OpenAUC.
