# Grad-CAM++ is Equivalent to Grad-CAM With Positive Gradients

arXiv: https://arxiv.org/abs/2205.10838 · HTML: https://mlsanity.com/paper/2205.10838/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 4 references that are in the map.
- First posted on arXiv 22 May 2022.

## Builds on (most cited first, 3 of 3)

- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Do ImageNet Classifiers Generalize to ImageNet?](https://mlsanity.com/paper/1902.10811/) (2019)

## Built on by (most cited first, 5 of 5)

- [Pre or Post-Softmax Scores in Gradient-based Attribution Methods, What is Best?](https://mlsanity.com/paper/2306.13197/) (2023)
- [Identifying Suspicious Regions of Covid-19 by Abnormality-Sensitive Activation Mapping](https://mlsanity.com/paper/2303.14901/) (2023)
- [EVolutionary Independent DEtermiNistiC Explanation](https://mlsanity.com/paper/2501.16357/) (2025)
- [Metric-Guided Synthesis of Class Activation Mapping](https://mlsanity.com/paper/2504.09998/) (2025)
- [Trustworthy AI-based crack-tip segmentation using domain-guided explanations](https://mlsanity.com/paper/2507.20658/) (2025)

## Abstract (arXiv)

The Grad-CAM algorithm provides a way to identify what parts of an image contribute most to the output of a classifier deep network. The algorithm is simple and widely used for localization of objects in an image, although some researchers have point out its limitations, and proposed various alternatives. One of them is Grad-CAM++, that according to its authors can provide better visual explanations for network predictions, and does a better job at locating objects even for occurrences of multiple object instances in a single image. Here we show that Grad-CAM++ is practically equivalent to a very simple variation of Grad-CAM in which gradients are replaced with positive gradients.