  Machine learning models provide statistically impressive results which might
be individually unreliable. To provide reliability, we propose an Epistemic
Classifier (EC) that can provide justification of its belief using support from
the training dataset as well as quality of reconstruction. Our approach is
based on modified variational auto-encoders that can identify a semantically
meaningful low-dimensional space where perceptually similar instances are close
in $\ell_2$-distance too. Our results demonstrate improved reliability of
predictions and robust identification of samples with adversarial attacks as
compared to baseline of softmax-based thresholding.
