Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Manifold-valued Image Generation with Wasserstein Generative Adversarial
  Nets   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.

23
The categories of this paper are: solv-int
37
The categories of this paper are: supr-con
29
The categories of this paper are: funct-an
34
The categories of this paper are: chem-ph
13
The categories of this paper are: quant-ph
11
The categories of this paper are: hep-lat
21
The categories of this paper are: adap-org
0
The categories of this paper are: hep-ph
22
The categories of this paper are: chao-dyn
15
The categories of this paper are: nucl-ex
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The categories of this paper are: acc-phys
19
The categories of this paper are: eess
2
The categories of this paper are: math
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
18
The categories of this paper are: econ