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Image regression: How to visualize the feature importance of an image in convolutional neural networks

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To investigate trained networks, we can use visualization techniques such as Grad-CAM, occlusion sensitivity, LIME, and deep dream.
But the input of these functions require to be string, char, categorical, cell.
For example, map = occlusionSensitivity(net,img,Y), where Y was the predicted value for img, however, this function shows error: Expected input number 3 to be one of these types: string, char, categorical, cell.
Could anyone tell me how to use these functions for regression analysis?

Answers (1)

Aditya Patil
Aditya Patil on 31 Mar 2021
the third parameter for occlusionSensitivity is label which was predicted for the model. For example,
label = classify(net,X);
scoreMap = occlusionSensitivity(net,X,label);
See the occlusionSensitivity doc page for more details.

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