  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, the previous works still have their own drawbacks. In
this paper, we propose a novel architecture, LFI-CAM, which is trainable for
image classification and visual explanation in an end-to-end manner. LFI-CAM
generates an attention map for visual explanation during forward propagation,
at the same time, leverages the attention map to improve the classification
performance through the attention mechanism. Our Feature Importance Network
(FIN) focuses on learning the feature importance instead of directly learning
the attention map to obtain a more reliable and consistent attention map. We
confirmed that LFI-CAM model is optimized not only by learning the feature
importance but also by enhancing the backbone feature representation to focus
more on important features of the input image. Experimental results show that
LFI-CAM outperforms the baseline models's accuracy on the classification tasks
as well as significantly improves on the previous works in terms of attention
map quality and stability over different hyper-parameters.
