Classifier-agnostic saliency map extraction
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We argue for the importance of decoupling saliency map extraction from any specific classifier. We propose a practical algorithm to train a classifier-agnostic saliency mapping by simultaneously training a classifier and a saliency mapping. The proposed algorithm is motivated as finding the mapping that is not strongly coupled with any specific classifier. We qualitatively and quantitatively evaluate the proposed approach and verify that it extracts higher quality saliency maps compared to the existing approaches that are dependent on a fixed classifier. The proposed approach performs well even on images containing objects from classes unseen during training.
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