The need for Explainable AI is increasing with the development of deep
learning. The saliency maps derived from convolutional neural networks
generally fail in localizing with accuracy the image features justifying the
network prediction. This is because those maps are either low-resolution as for
CAM [Zhou et al., 2016], or smooth as for perturbation-based methods [Zeiler
and Fergus, 2014], or do correspond to a large number of widespread peaky spots
as for gradient-based approaches [Sundararajan et al., 2017, Smilkov et al.,
2017]. In contrast, our work proposes to combine the information from earlier
network layers with the one from later layers to produce a high resolution
Class Activation Map that is competitive with the previous art in term of
insertion-deletion faithfulness metrics, while outperforming it in term of
precision of class-specific features localization.

深度学习中越来越需要可解释的 AI。我们的工作旨在将来自较早和较晚网络层的信息结合起来，产生具有竞争力的高分辨率 Class Activation Map，从而准确定位图像特征，从而超越以前的艺术水平。