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arXiv · 1805.08957

Semi-Supervised Learning with GANs: Revisiting Manifold Regularization

Bruno Lecouat·Chuan-Sheng Foo·Houssam Zenati·Vijay R. Chandrasekhar
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Abstract

GANS are powerful generative models that are able to model the manifold of natural images. We leverage this property to perform manifold regularization by approximating the Laplacian norm using a Monte Carlo approximation that is easily computed with the GAN. When incorporated into the feature-matching GAN of Improved GAN, we achieve state-of-the-art results for GAN-based semi-supervised learning on the CIFAR-10 dataset, with a method that is significantly easier to implement than competing methods.

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Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay R. Chandrasekhar. 2018-05-23. Semi-Supervised Learning with GANs: Revisiting Manifold Regularization. https://arxiv.org/abs/1805.08957

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