BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty

• URL: http://arxiv.org/abs/2007.06096v1
• Date: Sun, 12 Jul 2020 20:52:55 GMT
• Title: BaCOUn: Bayesian Classifers with Out-of-Distribution Uncertainty
• Authors: Th\'eo Gu\'enais, Dimitris Vamvourellis, Yaniv Yacoby, Finale Doshi-Velez, Weiwei Pan
• Abstract summary: We propose a Bayesian framework to obtain reliable uncertainty estimates for deep classifiers. Our approach consists of a plug-in "generator" used to augment the data with an additional class of points that lie on the boundary of the training data, followed by Bayesian inference on top of features that are trained to distinguish these "out-of-distribution" points.
• Score: 23.100727871427367
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Traditional training of deep classifiers yields overconfident models that are not reliable under dataset shift. We propose a Bayesian framework to obtain reliable uncertainty estimates for deep classifiers. Our approach consists of a plug-in "generator" used to augment the data with an additional class of points that lie on the boundary of the training data, followed by Bayesian inference on top of features that are trained to distinguish these "out-of-distribution" points.

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