HayCAM: A Novel Visual Explanation for Deep Convolutional Neural Networks

Abstract

Explaining the decision mechanism of Deep Convolutional Neural Networks (CNNs) is a new and challenging area because of the “Black Box” nature of CNN's. Class Activation Mapping (CAM) as a visual explainable method is used to highlight important regions of input images by using classification gradients. The lack of the current methods is to use all of the filters in the last convolutional layer which causes scattered and unfocused activation mapping. HayCAM as a novel visualization method provides better activation mapping and therefore better localization by using dimension reduction. It has been shown with mask detection use case that input images are fed into the CNN model and bounding boxes are drawn over the generated activation maps (i.e. weakly-supervised object detection) by three different CAM methods. IoU values are obtained as 0.1922 for GradCAM, 0.2472 for GradCAM++, 0.3386 for EigenCAM, and 0.3487 for the proposed HayCAM. The results show that HayCAM achieves the best activation mapping with dimension reduction.

Improving Explainability in CNN-Based Classification of Mask Images with HayCAM+: An Enhanced Visual Explanation Technique

“…The value of "n" can be chosen manually, as is the case in HayCAM. For further information, please refer to the study of Örnek and Ceylan [21].…”

“…In our previous work, we proposed HayCAM [21] as a visual XAI method and compared it to other well-known methods such as GradCAM [22], EigenCAM [23], and GradCAM++ [24]. Our primary contribution was to reduce the last layer of the deep model during visualization to ignore irrelevant filters and obtain a more focused activation map.…”