  With the wide use of deep neural networks (DNN), model interpretability has
become a critical concern, since explainable decisions are preferred in
high-stake scenarios. Current interpretation techniques mainly focus on the
feature attribution perspective, which are limited in indicating why and how
particular explanations are related to the prediction. To this end, an
intriguing class of explanations, named counterfactuals, has been developed to
further explore the "what-if" circumstances for interpretation, and enables the
reasoning capability on black-box models. However, generating counterfactuals
for raw data instances (i.e., text and image) is still in the early stage due
to its challenges on high data dimensionality and unsemantic raw features. In
this paper, we design a framework to generate counterfactuals specifically for
raw data instances with the proposed Attribute-Informed Perturbation (AIP). By
utilizing generative models conditioned with different attributes,
counterfactuals with desired labels can be obtained effectively and
efficiently. Instead of directly modifying instances in the data space, we
iteratively optimize the constructed attribute-informed latent space, where
features are more robust and semantic. Experimental results on real-world texts
and images demonstrate the effectiveness, sample quality as well as efficiency
of our designed framework, and show the superiority over other alternatives.
Besides, we also introduce some practical applications based on our framework,
indicating its potential beyond the model interpretability aspect.
