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We present an analysis of predictive uncertainty based out-of-distributiondetection for different approaches to estimate various models' epistemicuncertainty and contrast it with extreme value theory based open setrecognition. While the former alone does not seem to be enough to overcome thischallenge, we demonstrate that uncertainty goes hand in hand with the lattermethod. This seems to be particularly reflected in a generative model approach,where we show that posterior based open set recognition outperformsdiscriminative models and predictive uncertainty based outlier rejection,raising the question of whether classifiers need to be generative in order toknow what they have not seen.

Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?