  One of the significant challenges of deep neural networks is that the complex
nature of the network prevents human comprehension of the outcome of the
network. Consequently, the applicability of complex machine learning models is
limited in the safety-critical domains, which incurs risk to life and property.
To fully exploit the capabilities of complex neural networks, we propose a
non-intrusive interpretability technique that uses the input and output of the
model to generate a saliency map. The method works by empirically optimizing a
randomly initialized input mask by localizing and weighing individual pixels
according to their sensitivity towards the target class. Our experiments show
that the proposed model interpretability approach performs better than the
existing saliency map-based approaches methods at localizing the relevant input
pixels.
  Furthermore, to obtain a global perspective on the target-specific
explanation, we propose a saliency map reconstruction approach to generate
acceptable variations of the salient inputs from the space of input data
distribution for which the model outcome remains unaltered. Experiments show
that our interpretability method can reconstruct the salient part of the input
with a classification accuracy of 89%.
