  A CF explainer identifies the minimum modifications in the input that would
alter the model's output to its complement. In other words, a CF explainer
computes the minimum modifications required to cross the model's decision
boundary. Current deep generative CF models often work with user-selected
features rather than focusing on the discriminative features of the black-box
model. Consequently, such CF examples may not necessarily lie near the decision
boundary, thereby contradicting the definition of CFs. To address this issue,
we propose in this paper a novel approach that leverages saliency maps to
generate more informative CF explanations. Source codes are available at:
https://github.com/Amir-Samadi//Saliency_Aware_CF.
