InfoGAN: Interpretable Representation Learning by ...
the generator distribution P G by transforming a noise variable z P noise (z) into a sample G (z). This generator is trained by playing against an adversarial discriminator network D that aims to distinguish between samples from the true data distribution P data and the generator's distribution P G .
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