Name
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. |
Year | Venue | Field |
|---|---|---|
2019 | THIRTY-THIRD AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE / THIRTY-FIRST INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE / NINTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | HSL and HSV,Image generation,Pattern recognition,Computer science,Generative modeling,Artificial intelligence,Generative grammar,Manifold,Color image,Adversarial system |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
20 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiwu Huang | 1 | 252 | 15.26 |
| Jiqing Wu | 2 | 28 | 3.14 |
| Luc Van Gool | 3 | 27566 | 1819.51 |