IS-CAM: Integrated Score-CAM for axiomatic-based explanations

Rakshit Naidu, Ankita Ghosh, Yash Maurya, Shamanth R Nayak K, Soumya Snigdha Kundu

Convolutional Neural Networks have been known as black-box models as humans cannot interpret their inner functionalities. With an attempt to make CNNs more interpretable and trustworthy, we propose IS-CAM (Integrated Score-CAM), where we introduce the integration operation within the Score-CAM pipeline to achieve visually sharper attribution maps quantitatively. Our method is evaluated on 2000 randomly selected images from the ILSVRC 2012 Validation dataset, which proves the versatility of IS-CAM to account for different models and methods.