  This paper proposes a new gradient-based XAI method called Guided
AbsoluteGrad for saliency map explanations. We utilize both positive and
negative gradient magnitudes and employ gradient variance to distinguish the
important areas for noise deduction. We also introduce a novel evaluation
metric named ReCover And Predict (RCAP), which considers the Localization and
Visual Noise Level objectives of the explanations. We propose two propositions
for these two objectives and prove the necessity of evaluating them. We
evaluate Guided AbsoluteGrad with seven gradient-based XAI methods using the
RCAP metric and other SOTA metrics in three case studies: (1) ImageNet dataset
with ResNet50 model; (2) International Skin Imaging Collaboration (ISIC)
dataset with EfficientNet model; (3) the Places365 dataset with DenseNet161
model. Our method surpasses other gradient-based approaches, showcasing the
quality of enhanced saliency map explanations through gradient magnitude.
