Despite the tremendous achievements of deep convolutional neural networks
(CNNs) in many computer vision tasks, understanding how they actually work
remains a significant challenge. In this paper, we propose a novel two-step
understanding method, namely Salient Relevance (SR) map, which aims to shed
light on how deep CNNs recognize images and learn features from areas, referred
to as attention areas, therein. Our proposed method starts out with a
layer-wise relevance propagation (LRP) step which estimates a pixel-wise
relevance map over the input image. Following, we construct a context-aware
saliency map, SR map, from the LRP-generated map which predicts areas close to
the foci of attention instead of isolated pixels that LRP reveals. In human
visual system, information of regions is more important than of pixels in
recognition. Consequently, our proposed approach closely simulates human
recognition. Experimental results using the ILSVRC2012 validation dataset in
conjunction with two well-established deep CNN models, AlexNet and VGG-16,
clearly demonstrate that our proposed approach concisely identifies not only
key pixels but also attention areas that contribute to the underlying neural
network's comprehension of the given images. As such, our proposed SR map
constitutes a convenient visual interface which unveils the visual attention of
the network and reveals which type of objects the model has learned to
recognize after training. The source code is available at
this https URL

本论文提出了一种新的二步解释方法 —— SR map，可以更好地理解深度卷积神经网络如何识别图像并从所谓的注意区域中学习特征。该方法清楚地表明了哪些关键像素和注意力区域对于深度卷积神经网络理解给定的图像是有贡献的。