  To understand the black-box characteristics of deep networks, counterfactual
explanation that deduces not only the important features of an input space but
also how those features should be modified to classify input as a target class
has gained an increasing interest. The patterns that deep networks have learned
from a training dataset can be grasped by observing the feature variation among
various classes. However, current approaches perform the feature modification
to increase the classification probability for the target class irrespective of
the internal characteristics of deep networks. This often leads to unclear
explanations that deviate from real-world data distributions. To address this
problem, we propose a counterfactual explanation method that exploits the
statistics learned from a training dataset. Especially, we gradually construct
an explanation by iterating over masking and composition steps. The masking
step aims to select an important feature from the input data to be classified
as a target class. Meanwhile, the composition step aims to optimize the
previously selected feature by ensuring that its output score is close to the
logit space of the training data that are classified as the target class.
Experimental results show that our method produces human-friendly
interpretations on various classification datasets and verify that such
interpretations can be achieved with fewer feature modification.
