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Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect
Despite being impactful on a variety of problems and applications, the
generative adversarial nets (GANs) are remarkably difficult to train. This
issue is formally analyzed by \cite{arjovsky2017towards}, who also propose an
alternative direction to avoid the caveats in the minmax two-player training of
GANs. The corresponding algorithm, called Wasserstein GAN (WGAN), hinges on the
1-Lipschitz continuity of the discriminator. In this paper, we propose a novel
approach to enforcing the Lipschitz continuity in the training procedure of
WGANs. Our approach seamlessly connects WGAN with one of the recent
semi-supervised learning methods. As a result, it gives rise to not only better
photo-realistic samples than the previous methods but also state-of-the-art
semi-supervised learning results. In particular, our approach gives rise to the
inception score of more than 5.0 with only 1,000 CIFAR-10 images and is the
first that exceeds the accuracy of 90% on the CIFAR-10 dataset using only 4,000
labeled images, to the best of our knowledge.Comment: Accepted as a conference paper in International Conference on
Learning Representation(ICLR). Xiang Wei and Boqing Gong contributed equally
in this wor
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