Uncertainty-Aware Deep Classifiers Using Generative Models

This work tackles a familiar weakness of deep models: issuing highly confident outputs even when the input does not resemble the training distribution.

By combining classification and generative modelling, it proposes a more reliable way to separate known cases, doubtful cases, and unfamiliar inputs.