Real time image saliency for black box classifiers

Conference item

In this work we develop a fast saliency detection method that can be applied to any differentiable image classifier. We train a masking model to manipulate the scores of the classifier by masking salient parts of the input image. Our model generalises well to unseen images and requires a single forward pass to perform saliency detection, therefore suitable for use in real-time systems. We test our approach on Cifar-10 and ImageNet datasets and show that the produced saliency maps are easily interpretable, sharp, and free of artifacts. We suggest a new metric for saliency and test our method on the ImageNet object localisation task. We achieve results outperforming other weakly supervised methods.

Authors: Dabkowski, P; Gal, Y

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: NIPS Foundation
• Host title: Advances in Neural Information Processing Systems 31 (NIPS 2017)
• Journal: Advances in Neural Information Processing Systems 31 (NIPS 2017)
• Publication date: 2018-07-01
• Acceptance date: 2017-09-04