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I have a query regarding the baseline image and hope someone would help me out with this.
Let's say I am using AlexNet for image classification and want to find the importance of input pixels based on its predictions. As we know, AlexNet expects the input image to be normalized using mean and std, as mentioned here.
So, now I can think of 2 baseline images which seem to fit in the definition of black (all zero) image, as defined in the Integrated Gradients paper.
We can use the 'Average Image' as the baseline. So, if we normalize this baseline, we would essentially be feeding black (all zero) input to the network. (This seems more likely possibility to me).
We can use 'all zero' image as the baseline. So, after normalization, the actual image that we would feed to the network would have values equal to (-mean/std).
It would be really helpful to me if someone could clarify this.
Thanks,
Naman
The text was updated successfully, but these errors were encountered:
I have a query regarding the baseline image and hope someone would help me out with this.
Let's say I am using AlexNet for image classification and want to find the importance of input pixels based on its predictions. As we know, AlexNet expects the input image to be normalized using mean and std, as mentioned here.
So, now I can think of 2 baseline images which seem to fit in the definition of black (all zero) image, as defined in the Integrated Gradients paper.
It would be really helpful to me if someone could clarify this.
Thanks,
Naman
The text was updated successfully, but these errors were encountered: