  Attribution methods have been shown as promising approaches for identifying
key features that led to learned model predictions. While most existing
attribution methods rely on a baseline input for performing feature
perturbations, limited research has been conducted to address the baseline
selection issues. Poor choices of baselines limit the ability of one-vs-one
(1-vs-1) explanations for multi-class classifiers, which means the attribution
methods were not able to explain why an input belongs to its original class but
not the other specified target class. 1-vs-1 explanation is crucial when
certain classes are more similar than others, e.g. two bird types among
multiple animals, by focusing on key differentiating features rather than
shared features across classes. In this paper, we present GAN-based Model
EXplainability (GANMEX), a novel approach applying Generative Adversarial
Networks (GAN) by incorporating the to-be-explained classifier as part of the
adversarial networks. Our approach effectively selects the counterfactual
baseline as the closest realistic sample belong to the target class, which
allows attribution methods to provide true 1-vs-1 explanations. We showed that
GANMEX baselines improved the saliency maps and led to stronger performance on
perturbation-based evaluation metrics over the existing baselines. Existing
attribution results are known for being insensitive to model randomization, and
we demonstrated that GANMEX baselines led to better outcome under the cascading
randomization of the model.
