A saliency map identifies what regions in an image are the most important to a neural network's prediciton. XRAI is a saliency method that uses regions and colors to interpret how a computer understands an image. Typically, the image is split into a gradient of colors that represent what features in the image are most important to identifying the class of the image all the way to what features are least important to identifying the class of the image.
1 Choose a stylized image:
Prediction: ; % confidence
Original image:
Prediction: ; % confidence
Compute the average saliency map
1 Choose an Image Class:
2 Select the images below to include in the average:
The AI makes predictions based on regions it feels are important to identifying the image. What regions of the image do you think are most important to identifying this as a Doberman Pinscher? What regions are important and somewhat important? Finally, what regions are least important?
= This is how the AI sees
1Select models to compare
Comparison method: Intersect XRAI
2Select stylized image
Model Comparison: