# Extending Class Activation Mapping Using Gaussian Receptive Field

arXiv: https://arxiv.org/abs/2001.05153 · HTML: https://mlsanity.com/paper/2001.05153/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2020.

## Built on by (most cited first, 3 of 3)

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- [Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators](https://mlsanity.com/paper/2404.00437/) (2024)
- [Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving](https://mlsanity.com/paper/2502.14416/) (2025)

## Abstract (arXiv)

This paper addresses the visualization task of deep learning models. To improve Class Activation Mapping (CAM) based visualization method, we offer two options. First, we propose Gaussian upsampling, an improved upsampling method that can reflect the characteristics of deep learning models. Second, we identify and modify unnatural terms in the mathematical derivation of the existing CAM studies. Based on two options, we propose Extended-CAM, an advanced CAM-based visualization method, which exhibits improved theoretical properties. Experimental results show that Extended-CAM provides more accurate visualization than the existing methods.