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[D] Why does pix2pix generate a conditional *distribution* instead of a delta function?

Apologies if it’s too trivial.

Consider paired training data (x,y) where x is edge-image and y is realistic image.

Pix2Pix discriminator training is totally paired i.e. for a given x, y forms the real image and fake_y=G(x) forms the fake image. Note that this is deviating from Conditional GAN paper where for a single label of MNIST say 4, we have a distribution of real images available.

Also, note that the discriminator is pix2pix is conditional on x i.e. D(x,y) and D(x,fake_y) are to be discriminated. So, in principle pix2pix can learn a 1-1 mapping from edges to corresponding shoes in training set and I see no reason why it must produce a variation of shoes as shown in paper.

Assuming this code is correct implementation: https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/models/pix2pix_model.py


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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.