LFI-CAM: Learning Feature Importance for Better Visual Explanation

@article{Lee2021LFICAMLF,
  title={LFI-CAM: Learning Feature Importance for Better Visual Explanation},
  author={Kwang Hee Lee and Chaewon Park and Jung Hyun Oh and Nojun Kwak},
  journal={2021 IEEE/CVF International Conference on Computer Vision (ICCV)},
  year={2021},
  pages={1335-1343}
}
Class Activation Mapping (CAM) is a powerful technique used to understand the decision making of Convolutional Neural Network (CNN) in computer vision. Recently, there have been attempts not only to generate better visual explanations, but also to improve classification performance using visual explanations. However, previous works still have their own drawbacks. In this paper, we propose a novel architecture, LFI-CAM*** (Learning Feature Importance Class Activation Mapping), which is trainable… 
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