关键词: 图像分类, 分类激活图（CAM）, 多尺度, 可视化, 可解释性

Abstract: Classification activation map (CAM) has problems such as sparseness, discontinuity, incompleteness, etc.,and most of the current research only uses it for visual analysis. Based on this, this paper firstly utilizes the dilated convolution to design an automatic weighted multi-scale feature learning method in order to compensate for the defects of CAM and combines the multi-scale feature with the generation method of CAM to develop a multi-scale CAM generation method. Further, this paper embeds the multi-scale CAM into the network to form an end-to-end structure in order to enhance the classification performance. Taking the ResNet as the backbone, this paper proposes a classification enhancement model, ResNet-CE. Extensively experiments are conducted with ResNet-CE on three publicly available datasets, CIFAR10, CIFAR100 and STL10. Experiments show that the classification performance of ResNet-CE on these three datasets is significantly improved compared with the ResNet with similar parameters quantity. The error rates are reduced by 0.23%, 3.56% and 7.96%, respectively and the classification performance is better than most mainstream classification models. The proposed model can be easily transferred to the off-the-shelf model to improve its classification performance. At the same time, the algorithm retains the function of visualization and interpretation of the judgment of the model, which has certain application value and significance in scenes, such as diseases recognition in medical image and scene recognition in unmanned driving, etc.

Key words: image classification, classification activation map (CAM), multiscale, visualization, interpretability