Smooth Deep Saliency

arXiv:2404.02282v1 Announce Type: new
Abstract: In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling, with the purpose of explaining how a deep learning model detects tumors in scanned histological tissue samples. Those methods make the investigated models more interpretable for gradient-based saliency maps, computed in hidden layers. We test our approach on different models trained for image classification on ImageNet1K, and models trained for tumor detection on Camelyon16 and in-house real-world …

abstract arxiv cs.cv deep learning downsampling gradient hidden maps noise reduce samples tumors type work