  Existing uncertainty modeling approaches try to detect an out-of-distribution
point from the in-distribution dataset. We extend this argument to detect
finer-grained uncertainty that distinguishes between (a). certain points, (b).
uncertain points but within the data distribution, and (c). out-of-distribution
points. Our method corrects overconfident NN decisions, detects outlier points
and learns to say ``I don't know'' when uncertain about a critical point
between the top two predictions. In addition, we provide a mechanism to
quantify class distributions overlap in the decision manifold and investigate
its implications in model interpretability.
  Our method is two-step: in the first step, the proposed method builds a class
distribution using Kernel Activation Vectors (kav) extracted from the Network.
In the second step, the algorithm determines the confidence of a test point by
a hierarchical decision rule based on the chi-squared distribution of squared
Mahalanobis distances.
  Our method sits on top of a given Neural Network, requires a single scan of
training data to estimate class distribution statistics, and is highly scalable
to deep networks and wider pre-softmax layer. As a positive side effect, our
method helps to prevent adversarial attacks without requiring any additional
training. It is directly achieved when the Softmax layer is substituted by our
robust uncertainty layer at the evaluation phase.
