Classifier-agnostic saliency map extraction
作者：
Highlights：
• We propose a novel method to train a model indicating salient locations in the image.
• Our method produces a model which is not coupled with any specific classifier.
• We set the new best performance in weakly supervised localization on ImageNet.
• Our method does not require the true object class at inference time.
• We provide the code reproducing our results to let others built upon our work.
摘要
•We propose a novel method to train a model indicating salient locations in the image.•Our method produces a model which is not coupled with any specific classifier.•We set the new best performance in weakly supervised localization on ImageNet.•Our method does not require the true object class at inference time.•We provide the code reproducing our results to let others built upon our work.
论文关键词：Saliency map,Convolutional neural networks,Image classification,Weakly supervised localization
论文评审过程：Received 18 August 2019, Revised 13 April 2020, Accepted 17 April 2020, Available online 21 April 2020, Version of Record 29 April 2020.
论文官网地址：https://doi.org/10.1016/j.cviu.2020.102969
