December 2019
Intermediate to advanced
468 pages
14h 28m
English
To understand the Wasserstein GAN (WGAN, https://arxiv.org/abs/1701.07875), let's recall that, in the Training GANs section, we denoted the probability distribution of the generator with
and the probability distribution of the real data with
. In the process of training the GAN model, we update the generator weights and so we change
. The goal of the GAN framework is to converge to (this is also valid for other types of generative ...
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