Guiding InfoGAN with Semi-supervision

@inproceedings{Spurr2017GuidingIW,
  title={Guiding InfoGAN with Semi-supervision},
  author={Adrian Spurr and Emre Aksan and Otmar Hilliges},
  booktitle={ECML/PKDD},
  year={2017}
}
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. [...] Key Method The architecture builds on Information Maximizing Generative Adversarial Networks (InfoGAN) and is shown to learn both continuous and categorical codes and achieves higher quality of synthetic samples compared to fully unsupervised settings. Furthermore, we show that using small amounts of labeled data speeds-up training…Expand
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