  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.
