  Although deep neural network (DNN) has achieved many state-of-the-art
results, estimating the uncertainty presented in the DNN model and the data is
a challenging task. Problems related to uncertainty such as classifying unknown
classes (class which does not appear in the training data) data as known class
with high confidence, is critically concerned in the safety domain area (e.g,
autonomous driving, medical diagnosis). In this paper, we show that applying
current Bayesian Neural Network (BNN) techniques alone does not effectively
capture the uncertainty. To tackle this problem, we introduce a simple way to
improve the BNN by using one class classification (in this paper, we use the
term "set classification" instead). We empirically show the result of our
method on an experiment which involves three datasets: MNIST, notMNIST and
FMNIST.
