InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
https://arxiv.org/abs/1606.03657

University of Michigan - Ann Arbor

InfoGAN is an information-theoretic extension to the GAN that is able to learn disentangled representations in an unsupervised manner. InfoGANs are used when your dataset is very complex, when you’d like to train a cGAN and the dataset is not labelled, and when you’d like to see the most important features of your images.

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