Despite the tremendous success of convolutional neural networks (CNNs) in
computer vision, the mechanism of CNNs still lacks clear interpretation.
Currently, class activation mapping (CAM), a famous visualization technique to
interpret CNN's decision, has drawn increasing attention. Gradient-based CAMs
are efficient while the performance is heavily affected by gradient vanishing
and exploding. In contrast, gradient-free CAMs can avoid computing gradients to
produce more understandable results. However, existing gradient-free CAMs are
quite time-consuming because hundreds of forward interference per image are
required. In this paper, we proposed Cluster-CAM, an effective and efficient
gradient-free CNN interpretation algorithm. Cluster-CAM can significantly
reduce the times of forward propagation by splitting the feature maps into
clusters in an unsupervised manner. Furthermore, we propose an artful strategy
to forge a cognition-base map and cognition-scissors from clustered feature
maps. The final salience heatmap will be computed by merging the above
cognition maps. Qualitative results conspicuously show that Cluster-CAM can
produce heatmaps where the highlighted regions match the human's cognition more
precisely than existing CAMs. The quantitative evaluation further demonstrates
the superiority of Cluster-CAM in both effectiveness and efficiency.

本文提出了一种新的基于聚类的 CNN 解释算法 Cluster-CAM，它可以分离特征图并采用认知基础映射和认知剪切策略来合并认知图生成显著性热力图，结果显示 Cluster-CAM 比现有的 CAM 能够更准确地匹配人类认知，且在效率和效果上都具有显著优势。