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2016

Semi-Supervised Learning with Generative Adversarial Networks

Odena, Augustus

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We extend Generative Adversarial Networks (GANs) to the semi-supervised context by forcing the discriminator network to output class labels.

We train a generative model G and a discriminator D on a dataset with inputs belonging to one of N classes.
At training time, D is made to predict which of N+1 classes the input belongs to, where an extra class is added to correspond to the outputs of G.
We show that this method can be used to create a more data-efficient classifier and that it allows for generating higher quality samples than a regular GAN.

Semi-Supervised Learning with Generative Adversarial Networks · Around

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Gradient-based learning applied to document recognition
LeCun, Yann, Bottou, Léon, Bengio, Yoshua, and Haffner, Patrick · 1998
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Kingma, D. P and Welling, M · 2013
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Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Semi-supervised learning with deep generative models
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Kingma, Diederik P., Rezende, Danilo Jimenez, Mohamed, Shakir, and Welling, Max · 2014
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