Instead, we’re going to break it down in a way that even your grandma could understand!

So what are integrated gradients and layer integrated gradients exactly? Well, they’re basically tools used by AI researchers to figure out which parts of an image or text input are most important for making predictions. And when we say “important,” we mean like really important the kind that can make or break your entire model!

Now, let me explain how it works in a way that even my cat could understand (because she’s pretty smart). Imagine you have this picture of a dog:


And let’s say your AI model is trying to predict whether the animal in the image is a cat or a dog (because that’s what all the cool kids are doing these days). But how does it know which parts of the picture matter most for making this prediction? That’s where integrated gradients come in!

First, we take our input image and create a series of “intermediate” images by gradually changing one pixel at a time. For example:


In this first intermediate image, we’ve changed the color of just one pixel (the red circle). And then we run our AI model on each of these images to see how it affects the output prediction:


In this second intermediate image, we’ve changed another pixel (the green circle). And so on and so forth until we have a series of images that look like this:


Now, here’s where the magic happens! We take these intermediate images and calculate something called an “integrated gradient” for each pixel in our original input image (the one with the dog). And what does this integrated gradient tell us? Well, it tells us how important that particular pixel is for making the final prediction.

So let’s say we have a really high integrated gradient score for the red circle in our first intermediate image:


That means this pixel is super important for helping our AI model figure out whether it’s looking at a cat or a dog! And if we change the color of that red circle to something else (like blue), then our integrated gradient score will go down:


And that’s all for today! Thanks for tuning in, and don’t forget to subscribe for more AI-related content coming your way soon!