We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular  gradient methods. It is also quantitatively similar and 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. Finally, 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. Since our method only requires a single forward pass through a few of the layers in a network, it is at least 97x faster than Guided Backprop and much more accurate. Using our method instead of Guided Backprop,  class activation methods such as Grad-CAM , Grad-CAM+ + and Smooth Grad-CAM + + will run several orders of magnitude faster, have a significantly smaller memory footprint and be more accurate . This will make such methods feasible on resource limited platforms such as robots, cell phones and low cost industrial devices. This will also significantly help them work in extremely data intensive applications such as satellite image processing. All without sacrificing accuracy. Our method is generally straight forward and should be applicable to the most commonly used CNNs. We also show examples of our method used to enhance Grad-CAM++ .
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 .
A
fine-resolution
R
and
or
R
Finally, we
We
D
Since our method only requires a single forward pass through a few of the layers in a network, it is at least 97x faster than Guided Backprop and much more accurate.
A
coarse-resolution
R
,
and
D
+ and Smooth Grad-CAM
R
+ will run several orders of magnitude faster, have a significantly smaller memory footprint and be more accurate
seem to yield demonstrably superior results without sacrificing speed
R
such
fine-resolution saliency
R
and low cost industrial devices. This will also significantly help them work in extremely data intensive applications such as satellite image processing. All without sacrificing accuracy. Our method is generally straight forward and should be applicable to the most commonly used CNNs. We also show examples of our method used to enhance Grad-CAM++
, low-cost industrial devices, astronomy and satellite imagery