6,482 research outputs found
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
Learning Generative Models across Incomparable Spaces
Generative Adversarial Networks have shown remarkable success in learning a
distribution that faithfully recovers a reference distribution in its entirety.
However, in some cases, we may want to only learn some aspects (e.g., cluster
or manifold structure), while modifying others (e.g., style, orientation or
dimension). In this work, we propose an approach to learn generative models
across such incomparable spaces, and demonstrate how to steer the learned
distribution towards target properties. A key component of our model is the
Gromov-Wasserstein distance, a notion of discrepancy that compares
distributions relationally rather than absolutely. While this framework
subsumes current generative models in identically reproducing distributions,
its inherent flexibility allows application to tasks in manifold learning,
relational learning and cross-domain learning.Comment: International Conference on Machine Learning (ICML
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