# Efficient Saliency Maps for Explainable AI

arXiv: https://arxiv.org/abs/1911.11293 · HTML: https://mlsanity.com/paper/1911.11293/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

## Built on by (most cited first, 30 of 30)

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- [A Novel Explainable Artificial Intelligence Model in Image Classification problem](https://mlsanity.com/paper/2307.04137/) (2023)
- [Bridging Idealized and Operational Models: An Explainable AI Framework for Earth System Emulators](https://mlsanity.com/paper/2510.13030/) (2025)
- [Improving Health Professionals' Onboarding with AI and XAI for Trustworthy Human-AI Collaborative Decision Making](https://mlsanity.com/paper/2405.16424/) (2024)
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- [An Automated Approach for Timely Diagnosis and Prognosis of Coronavirus Disease](https://mlsanity.com/paper/2104.14116/) (2021)
- [CathAI: Fully Automated Interpretation of Coronary Angiograms Using Neural Networks](https://mlsanity.com/paper/2106.07708/) (2021)
- [Explaining neural network predictions of material strength](https://mlsanity.com/paper/2111.03729/) (2021)
- [Testing RadiX-Nets: Advances in Viable Sparse Topologies](https://mlsanity.com/paper/2311.03609/) (2023)
- [Local Explanations for Clinical Search Engine results](https://mlsanity.com/paper/2110.12891/) (2021)
- [Identifying Suitable Tasks for Inductive Transfer Through the Analysis of Feature Attributions](https://mlsanity.com/paper/2202.01096/) (2022)
- [Generating detailed saliency maps using model-agnostic methods](https://mlsanity.com/paper/2209.09202/) (2022)
- [Symmetry Detection in Trajectory Data for More Meaningful Reinforcement Learning Representations](https://mlsanity.com/paper/2211.16381/) (2022)
- [Two-step interpretable modeling of Intensive Care Acquired Infections](https://mlsanity.com/paper/2301.11146/) (2023)
- [Reward Design for Justifiable Sequential Decision-Making](https://mlsanity.com/paper/2402.15826/) (2024)
- [Adapting Visualization Techniques for Time-Series Anomaly Detection: From Convolutional Neural Networks to Convolutional-Recurrent Neural Networks](https://mlsanity.com/paper/2411.04707/) (2024)
- [From Abstract to Actionable: Pairwise Shapley Values for Explainable AI](https://mlsanity.com/paper/2502.12525/) (2025)
- [Multiple Additive Neural Networks for Structured and Unstructured Data](https://mlsanity.com/paper/2604.26888/) (2026)
- [Explainable Artificial Intelligence (XAI) in Computational Pathology: Definitions, Taxonomy, and Recommendations](https://mlsanity.com/paper/2608.28820/) (2026)

## Abstract (arXiv)

We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular fine-resolution gradient methods. It is also quantitatively similar or better in accuracy. Our technique works by measuring information at the end of each network scale which is then combined into a single saliency map. We describe how saliency measures can be made more efficient by exploiting Saliency Map Order Equivalence. We visualize individual scale/layer contributions by using a Layer Ordered Visualization of Information. This provides an interesting comparison of scale information contributions within the network not provided by other saliency map methods. Using our method instead of Guided Backprop, coarse-resolution class activation methods such as Grad-CAM and Grad-CAM++ seem to yield demonstrably superior results without sacrificing speed. This will make fine-resolution saliency methods feasible on resource limited platforms such as robots, cell phones, low-cost industrial devices, astronomy and satellite imagery.