Efficient Saliency Maps for Explainable AI

TL;DR

This paper introduces an efficient saliency map method for deep CNNs that maintains or improves accuracy while being faster, enabling real-time explainability on resource-constrained devices.

Contribution

It proposes a novel, efficient saliency map technique based on information at network scales, exploiting Saliency Map Order Equivalence and Layer Ordered Visualization.

Findings

Outperforms popular gradient-based methods in efficiency and accuracy

Enables real-time saliency visualization on resource-limited platforms

Provides insights into scale contributions within CNNs

Abstract

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.