Manifold-valued image generation with Wasserstein Generative Adversarial Nets

Huang, Zhiwu, Wu, Jiqing and Van Gool, Luc (2019) Manifold-valued image generation with Wasserstein Generative Adversarial Nets. In AAAI-19, IAAI-19, EAAI-20: Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence. vol. 33, AAAI Press. pp. 3886-3893 . (doi:10.1609/aaai.v33i01.33013886).

Abstract

Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversarial Nets (WGANs), are studied on manifold-valued images that are frequently encountered in real-world applications. To fill the gap, this paper first formulates the problem of generating manifold-valued images and exploits three typical instances: hue-saturation-value (HSV) color image generation, chromaticity-brightness (CB) color image generation, and diffusion-tensor (DT) image generation. For the proposed generative modeling problem, we then introduce a theorem of optimal transport to derive a new Wasserstein distance of data distributions on complete manifolds, enabling us to achieve a tractable objective under the WGAN framework. In addition, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets. On the three datasets, we experimentally demonstrate the proposed manifold-aware WGAN model can generate more plausible manifold-valued images than its competitors.

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Contributors

Author: Zhiwu Huang

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