  In this work, we propose an introspection technique for deep neural networks
that relies on a generative model to instigate salient editing of the input
image for model interpretation. Such modification provides the fundamental
interventional operation that allows us to obtain answers to counterfactual
inquiries, i.e., what meaningful change can be made to the input image in order
to alter the prediction. We demonstrate how to reveal interesting properties of
the given classifiers by utilizing the proposed introspection approach on both
the MNIST and the CelebA dataset.
