Efficient Saliency Maps for Explainable AI

Understand

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.