Abstract: Convolutional neural networks play an important role in the field of computer vision,but their black box nature makes it difficult for people to understand the reasons for their decisions,seriously hindering their application in certain security areas.Traditional class activation mapping(CAM) algorithms are often limited by the interpretability of deep neurons,resulting in weaker interpretability of shallow neurons and the presence of significant noise.To address this challenge,we propose an interpretable shallow class activation mapping algorithm that can generate fine-grained explanations.This algorithm is based on the theory of correlation propagation,considering the correlation between adjacent layers,obtaining inter layer correlation weights,and using the feature map with spatial weights as a mask,multiplying it with inter layer correlation weights to achieve shallow interpretation.Experimental results show that compared with LayerCAM,which explains the shallow layer best,the proposed algorithm improves the comprehensive score of deletion and insertion tests for the class activation maps generated by each layer of the con-volutional neural network by a maximum of 2.73 and a minimum of 0.24 on the ILSVRC2012 val dataset,and a maximum of 1.31 and a minimum of 0.38 on the CUB-200-2011 dataset.

Key words: Class activation mapping algorithm, Convolutional neural network, Shallow neurons, Spatial weight, Interlayer correlation