  Deep learning models trained using massive amounts of data tend to capture
one view of the data and its associated mapping. Different deep learning models
built on the same training data may capture different views of the data based
on the underlying techniques used. For explaining the decisions arrived by
blackbox deep learning models, we argue that it is essential to reproduce that
model's view of the training data faithfully. This faithful reproduction can
then be used for explanation generation. We investigate two methods for data
view extraction: hill-climbing approach and a GAN-driven approach. We then use
this synthesized data for creating shadow models for explanation generation:
Decision-Tree model and Formal Concept Analysis based model. We evaluate these
approaches on a Blackbox model trained on public datasets and show its
usefulness in explanation generation.
