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I've read the paper about edge detection, in this paper they treat edge detection as a learning problem which takes an image patch as input and output a label, a binary edge map or a segmentation mask, but I don't know what is a segmentation mask?

In the paper their image patch is 32*32 but segmentation mask is 16*16, why is that?

During training they transform a multidimensional segmentation mask into a binary label to calculate the information gain, but when making predictions, how can I transform a binary label back to a segmentation mask or edge map? the transformation seems to be irreversible.

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