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

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W_Elsa
W_Elsa 2021-3-27
回答： Aditya Patil 2021-3-31
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?

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Aditya Patil
Aditya Patil 2021-3-31
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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