Guided AbsoluteGrad: Magnitude of Gradients Matters to Explanation's Localization and Saliency
<|reference_start|>Guided AbsoluteGrad: Magnitude of Gradients Matters to Explanation's Localization and Saliency: 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.<|reference_end|>
@article{huang2024guided,
    title={Guided AbsoluteGrad: Magnitude of Gradients Matters to Explanation's
  Localization and Saliency},
    author={Jun Huang, Yan Liu},
    journal={arXiv preprint arXiv:2404.15564},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.15564},
    primaryClass={cs.CV cs.AI cs.HC cs.LG}
}