Linus:

Why though?? Isn’t the point of feature visualization to understand how the weights behave for an image?


The reasoning can be found in the Smooth Grad-CAM ++ paper. They’re computing an average (aka. smoothed) sensitivity map over a small neighborhood of the input image. They make this “neighborhood” by creating multiple noisy versions of the input.



 Linus:

n my case, it seems that the noise is dominating and more or less the only thing I see is something related to this noise.


If the noise is dominating, then there might something wrong with your transformations. I would ensure whatever transformations you’re using are appropriate for the CNN model you’re using (looks like ResNet). Also, make sure you scale any images to [0, 1] (or [0, 255]`) for visualizing using matplotlib.
But honestly, there are so many things that could cause your problem, and it’s just a guessing game (at least it is for me) unless we have a reproducible example. Maybe you could make and share a minimal example on Google Colab? Looks like you’re already using Colab, so hopefully not too much of a problem to create a simple example that doesn’t require access to files in your Google Drive, etc.



 Linus:

Can I turn the noise completely off?


If you want a method without random additive noise, use GradCAMpp instead of SmoothGradCAMpp.