Guiding InfoGAN with Semi-Supervision

14 Jul 2017Adrian SpurrEmre AksanOtmar Hilliges

In this paper we propose a new semi-supervised GAN architecture (ss-InfoGAN) for image synthesis that leverages information from few labels (as little as 0.22%, max. 10% of the dataset) to learn semantically meaningful and controllable data representations where latent variables correspond to label categories... (read more)

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