UNCERTAINTY ESTIMATION WITH A VAE-CLASSIFIER HYBRID MODEL

We propose a hybrid model that combines a generative unit and a discriminative classifier to quantify uncertainty in a classification task. The representation learning capability in the VAE module allows our method to learn more useful and generalizable features and outperform other purely discriminative classifiers when training labels are limited. With proper statistical treatment, the probabilistic encoder in our VAE module offers a convenient mechanism to express uncertainty for out-of-distribution (OOD) data. As a result, our method gives better calibrated uncertainty prediction. We demonstrate the effectiveness of our method on MNIST and a challenging medical image dataset for skin lesion diagnosis.