# Class Activation Map Guided Backpropagation for Discriminative Explanations
## 2 Related Work  
In this section, we compile a comprehensive review of feature attribution methods from recent years and categorize them into gradient-based, perturbation-based, and class activation map-based approaches. Gradient-based methods involve calculating the gradients of the model's output with respect to the input image. For instance, the method proposed in [8] measures pixel importance based on the maximum absolute gradient. GBP [6] zeroes out gradients that are negative during backpropagation through the ReLU. RectGrad [7] addresses the noise in saliency maps by assuming it stems from less significant features with non-zero gradients post ReLU and proposes the setting of gradients to zero when their importance scores--defined as the product of gradients and activations--fall below a set threshold. However, GBP and RectGrad can produce similar or even identical saliency maps. Our work falls within this category of methods and is closely related to both GBP and RectGrad. In particular, we aim to enhance the category discrimination of RectGrad by guiding gradient selection at the spatial level via class activation maps. Moreover, some methods propose top-down rules for backpropagation [9, 10]. Beyond single-gradient approaches for saliency maps, some methods accumulate gradients over multiple samples to reduce noise [3, 4, 5]. Integrated Gradients (IG) [3] generates interpolated images along a straight path from an all-zero baseline to the actual input, averaging the gradients of these interpolated images. The gradients computed by IG are more reliable than vanilla gradients, and the resulting saliency maps reveal the model's behavior more accurately. However, in saturation regions, where the model's output changes minimally, the gradients become unreliable. Integrating these gradients harms the quality of saliency maps. IDG [5] addresses this by weighting gradients based on model output changes, focusing more on non-saturated regions. The choice of path function influences the resulting saliency maps. A straight path may not always be suitable, as it can lead to unreliable gradients. To address this, GIG [4] generates interpolated images along an adaptive path that minimizes the absolute derivative between the baseline and the input. Perturbation-based methods perturb the input image multiple times and observe the model's output to estimate every pixel's attribution. RISE [11] estimates the importance of each pixel by applying multiple random masks to the input image and weighting these masks based on the model's output. Mask [12] introduces an iterative optimization strategy that automatically learns an optimal mask to maximize the decrease in the model's output probability when specific image areas are covered. To avoid adversarial effects, both RISE and Mask require that the mask size be significantly smaller than the overall input image size. In this research vein, I-GOS [13] and I-GOS++ [14] employ an iterative optimization method like Mask but innovate by using integrated gradients instead of traditional gradient optimization. This approach enables the use of full-image masks, leading to the generation of more detailed saliency maps that accurately highlight the regions most impactful to the model's decision making. Class activation map-based methods weight feature maps to generate saliency maps, originating with CAM [15], which pioneers this category by linearly combining the last convolutional layer's feature maps to produce class activation maps. While effective in certain CNN structures, CAM's reliance on specific model architectures limits its broader applicability. To address this, researchers have developed CAM-derived methods [16, 17, 18] that weight feature maps differently, making them applicable to a wider range of CNN models. Furthermore, certain methods [19, 20] assign weights to individual features within the feature maps, allowing for more precise identification of areas critical to model predictions.