Extracting saliency maps, which indicate parts of the image important to classification, requires many tricks to achieve satisfactory performance when using classifier-dependent methods. Instead, we propose classifier-agnostic saliency map extraction, which finds all parts of the image that any classifier could use, not just one given in advance. We observe that the proposed approach extracts higher quality saliency maps and outperforms existing weakly-supervised localization techniques, setting the new state of the art result on the ImageNet dataset.
We propose a new saliency map extraction method which results in extracting higher quality maps. Proposes a classifier-agnostic method for saliency map extraction. This paper introduces a new saliency map extractor that seems to improve state-of-the-art results. The authors argue that when an extracted saliency map is directly dependent on a model, then it might not be useful for a different classifier, and suggests a scheme to approximate the solution.