# Attention Map-Guided Visual Explanations for Deep Neural Networks
## 2 Related Work  
### CAM-Based XAI Methods  
There are now many ways of using class activation mapping (CAM) [13] based methods for explaining the output of a model. These XAI methods use CAM methods as their basis, and some researchers upgrade CAM methods with a mix of backpropagation gradients and feature maps of a certain convolutional layer to generate an explanation map. To generate the explanation map, they have mainly used prior position information, such as part-level bounding boxes and segmentation masks [14]. The CAM is essentially a weighted linear sum of these visual patterns' existence in various spatial regions. It can determine the images' most important regions for the given category by simply upsampling the class activation map to the size of the input image. In Figure 2, the global average pooling (GAP) layer is used to convert the feature map into a feature vector, and each layer of the feature map can be represented as a numerical value. CAM methods multiply the weights corresponding to the bull mastiff class by the layers corresponding to the feature map, making a weighted linear sum. Using a CAM method, it is possible to observe which area the model is looking at. However, the CAM method has some shortcomings; e.g., it needs to change the model's structure from a fully connected layer to a global average pooling layer. Users are cautious to explain DNN models using the CAM technique, since it requires changing the model's basic structure. Changing the model's internal structure is not convenient for the user.  
Figure 1: An overview of attention map-guided visual explanations for deep neural networks.  
To address these problems of CAM methods, [PERSON] et al. [12] proposed using gradient calculations instead of GAP. Grad-CAM is a new method for combining feature maps using the gradient weights without any modifications to the network structure. It allows any gradients to flow into the final convolutional layer to build a coarse localization map that highlights the regions essential in the image for the predicting class. Grad-CAM assigns priority values to each neuron for a specific choice using the gradient information flowing into the last convolutional layer of the CNN model.  
CAM and Grad-CAM use a linear combination of activation to produce a fine-grained explanation. Grad-CAM++ is a Grad-CAM enhancement that provides a visual explanation for the associated class by using a weighted mixture of the positive partial derivatives of the target layers' feature maps concerning a predetermined class score as weights. To create an enhanced visual explanation of multiple objects in a single image, SmoothGrad-CAM was created [15], which is a simple method that can help visually sharpen gradient-based sensitivity maps. Additionally, it can visually brighten gradient-based sensitivity maps, which obtain random samples in the neighbor of an input x and average the sensitivity maps. The gradient of the class score function for the input image is a good starting point for SmoothGrad-CAM. [PERSON], D et al. [16] proposed SmoothGrad-CAM++, which combines SmoothGrad-CAM and Grad-CAM++. Smooth Grad-CAM++ creates visual explanations of the input images that are more visually sharp. Smooth Grad-CAM++ allows one to visualize a layer, a subset of feature maps, or a subset of neurons inside a feature map at each occurrence. Although these XAI methods can provide reasonable visualizations, the majority of them lack obvious and sufficient theoretical backing. XGrad-CAM [17] was proposed to satisfy those needs as much as is feasible, and the studies on it show that ot is a more sensitive and conservation-oriented variant of Grad-CAM. However, because the feature maps are not always connected to the target class, the outputs of activation-based approaches may collect too much worthless information.