Abstract
To intuitively and accurately understand the decision mechanism of Convolutional Neural Networks(CNN), CNN visualization, as an essential part of explainable deep learning, has gradually become a hot topic in artificial intelligence. There have been many achievements in CNN visualization research, such as Gradients, Deconvolution, Class Activation Maps(CAM), etc. But there has been no systematic comparative study on CNN visualization algorithms. The choice of visualization algorithm is critical for accurately explaining the decision process of CNNs. Therefore, an experimental evaluation research on representative CNN visualization algorithms is conducted in this paper for ResNet50 and VGG16 on Caltech101, ImageNet, and VOC2007. The visualization performance is assessed in four aspects: causality, anti-disturbance capability, usability, and computational complexity, and suggestions for selecting CNN visualization algorithms are proposed based on the experimental results.
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Xu, X., Tu, S., Xue, Y., Chai, L. (2023). Visualizing CNN: An Experimental Comparative Study. In: Lu, H., Blumenstein, M., Cho, SB., Liu, CL., Yagi, Y., Kamiya, T. (eds) Pattern Recognition. ACPR 2023. Lecture Notes in Computer Science, vol 14408. Springer, Cham. https://doi.org/10.1007/978-3-031-47665-5_17
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