# Class Activation Map Guided Backpropagation for Discriminative Explanations
## 1 Introduction  
Deep convolutional neural networks (CNNs) have made impressive strides in the field of computer vision. However, their complex internal mechanisms often appear as a \"gray box\", making them difficult for outsiders to understand. To shed light on the inner workings of these models, researchers have developed a range of feature attribution techniques. These methods create saliency maps that are the same size as the input images, highlighting the contribution of each pixel to the model's predictions.  
This paper focuses on gradient-based methods, a key subset of feature attribution techniques. They assess the importance of features by measuring the gradient of the model's output with respect to the input features. However, the phenomenon of gradient saturation can lead to significant features having small gradients, while less significant features may exhibit larger gradients [1]. This discrepancy decreases the reliability ofgradients in terms of feature importance and can cause them to appear as background noise in saliency maps, potentially misleading users and failing to accurately reflect the model's decision-making process.  
To generate more reliable saliency maps, some methods [2, 3, 4, 5] aggregate gradients from multiple forward-backward passes to produce the final saliency map but require hundreds of forward and backward passes, which is computationally expensive. Guided Backpropagation (GBP) [6] and RectGrad [7] aim to produce saliency maps via backpropagation only once. They selectively use only certain gradients that meet certain conditions during backpropagation to the Rectified Linear Unit (ReLU) and can significantly remove noise in the background. However, both generate visually similar saliency maps across different categories. In other words, they lack category discrimination.  
The lack of category discrimination means feature attribution methods can only reveal what the model has detected in the image rather than what the model emphasizes when explaining a specific category, thus failing to reveal the model's behavior. In this paper, we aim to enhance category discrimination. We believe RectGrad is a promising method and aim to enhance its category discrimination.  
To address the limitation of RectGrad's category discrimination, we propose a novel method, Class activation map Guided Back Propagation (CGBP). Unlike the threshold-based strategy in RectGrad, which may misidentify less significant features as important, CGBP leverages class activation maps to refine gradients at the spatial level. This refinement distinguishes significant from less significant features and retains only meaningful gradients. Using these refined gradients, CGBP employs the RectGrad methodology to generate saliency maps.  
Our experiments on the ImageNet validation set show that CGBP outperforms other methods in SIC, AIC, and IS metrics, offering clearer insights into the model's decision-making process. While its DS performance shows moderate improvement, CGBP demonstrates greater sensitivity to model parameter changes, as verified by a sanity check.  
This study offers the following contributions:  
1. We identify RectGrad's limitations in distinguishing feature gradients due to the inefficiencies of the threshold strategy.
2. We introduce CGBP, which leverages class activation maps for enhanced gradient discrimination and retention of significant features.
3. We validate CGBP's effectiveness through experiments, achieving state-of-the-art performance on SIC, AIC, and IS metrics, and demonstrate its sensitivity to model parameter changes via a sanity check.