  OOD detection has become more pertinent with advances in network design and
increased task complexity. Identifying which parts of the data a given network
is misclassifying has become as valuable as the network's overall performance.
We can compress the model with quantization, but it suffers minor performance
loss. The loss of performance further necessitates the need to derive the
confidence estimate of the network's predictions. In line with this thinking,
we introduce an Uncertainty Quantification(UQ) technique to quantify the
uncertainty in the predictions from a pre-trained vision model. We subsequently
leverage this information to extract valuable predictions while ignoring the
non-confident predictions. We observe that our technique saves up to 80% of
ignored samples from being misclassified. The code for the same is available
here.
