#StackBounty: #machine-learning #deep-learning #keras #r #convolutional-neural-network Calculate importance of input data bands for CNN…

Bounty: 50

I constructed and trained a convolutional neural network using Keras in R with the TensorFlow backend. I feed the network with multispectral images for a simple image classification.

Is there some way to calculate which of the input bands were most important for the classification task? Ideally, I would like to have a plot with some measure of importance, grouped by bands and image classes.

How can I obtain this information? Would it be necessary / possible to calculate saliency maps for every band and picture, and take the mean or sum of these images per class and band?

Are there also other ways to get the information, which band was most important for the classification of an image?

Edit: With saliency maps I mean these or these visualizations. They provide information on which part of the image led the CNN to the conclusion to which class it identifies. However, I always see only one saliency map for the whole image. Is it possible to make one for each input band of an image? For example if I input RGB data, one for each color channel?

(This is inspired by a visualization of this paper. I saw it but I don’t know if it’s valid to do and if yes, how to do.)


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