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

We present an analysis of predictive uncertainty based out-of-distribution detection for different approaches to estimate various models' epistemic uncertainty and contrast it with extreme value theory based open set recognition. While the former alone does not seem to be enough to overcome thi...

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Bibliographic Details
Main Authors: Mundt, Martin, Pliushch, Iuliia, Majumder, Sagnik, Ramesh, Visvanathan
Format: Conference Proceeding
Language:English
Subjects:
deep learning
Entropy
Mathematical model
neural network uncertainty
Neural networks
open set recogniton
out of distribution detection
Predictive models
Training
Uncertainty
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Summary:We present an analysis of predictive uncertainty based out-of-distribution detection for different approaches to estimate various models' epistemic uncertainty and contrast it with extreme value theory based open set recognition. While the former alone does not seem to be enough to overcome this challenge, we demonstrate that uncertainty goes hand in hand with the latter method. This seems to be particularly reflected in a generative model approach, where we show that posterior based open set recognition outperforms discriminative models and predictive uncertainty based outlier rejection, raising the question of whether classifiers need to be generative in order to know what they have not seen.
ISSN:2473-9944
DOI:10.1109/ICCVW.2019.00098