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(i simply added this for others seeing this later). So, no it is not just suited for only synthesis high quality human face synthesis but any image type Takes this analysis further and the phd thesis stability and expressiveness of deep generative models cantains a quite thorough and mathematical introduction to gans.
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Loss curves of generative adversarial networks (gans) are very difficult to interpret Generative adversarial networks, basically boil down to a combination of a generic generator and a discriminator trying to beat each other, so that the generator tries to generate much better images (usually from noise) and discriminator becomes much better at classification It depends on the type of gan used, the type of data used, the size of the networks, the size of the dataset, etc
Why are the generator and discriminator designed differently in the example my first gan of the coursera course
Build basic generative adversarial networks (gans)? Discriminator in the original gan is a regressor no, it is a classifier It classifies an image as real or fake, with the output usually being probability that the image is real (you could reverse this and use generated images as the target class, provided you change the generator training to match) Is it true with most of the (advanced or) contemporary gans
In wgans the w stands for. 1 if you ask this question it means you conceive a generative adversarial network as a combination of 2 separate entities, the discriminator and generator, but this is not really the case.
