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20162019
most citedBuilding Deep Networks on Grassmann Manifolds

46 citations · 86 across the 9 of their papers we have counts for

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Showing cs.CVShow all

8 papers · 1 filter

cs.CV2019★ 2 cited

Sliced Wasserstein Generative Models

Jiqing Wu, Zhiwu Huang, Dinesh Acharya +4

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is…

cs.CV2017★ 8 cited

Face Translation between Images and Videos using Identity-aware CycleGAN

Zhiwu Huang, Bernhard Kratzwald, Danda Pani Paudel +2

This paper presents a new problem of unpaired face translation between images and videos, which can be applied to facial video prediction and enhancement. In this problem there exi…

cs.CV2017★ 3 cited

Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets

Zhiwu Huang, Jiqing Wu, Luc Van Gool

Generative modeling over natural images is one of the most fundamental machine learning problems. However, few modern generative models, including Wasserstein Generative Adversaria…

cs.CV2017★ 5 cited

Wasserstein Divergence for GANs

Jiqing Wu, Zhiwu Huang, Janine Thoma +2

In many domains of computer vision, generative adversarial networks (GANs) have achieved great success, among which the family of Wasserstein GANs (WGANs) is considered to be state…

cs.CV2017★ 2 cited

Sliced Wasserstein Generative Models

Jiqing Wu, Zhiwu Huang, Dinesh Acharya +4

In generative modeling, the Wasserstein distance (WD) has emerged as a useful metric to measure the discrepancy between generated and real data distributions. Unfortunately, it is…

cs.CV2017★ 12 cited

On the Relation between Color Image Denoising and Classification

Jiqing Wu, Radu Timofte, Zhiwu Huang +1

Large amount of image denoising literature focuses on single channel images and often experimentally validates the proposed methods on tens of images at most. In this paper, we inv…

cs.CV2016★ 46 cited

Building Deep Networks on Grassmann Manifolds

Zhiwu Huang, Jiqing Wu, Luc Van Gool

Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks. In order to enable deep learning on Grassmann manifolds, this paper proposes a d…

cs.CV2016★ 1 cited

Generic 3D Convolutional Fusion for image restoration

Jiqing Wu, Radu Timofte, Luc Van Gool

Also recently, exciting strides forward have been made in the area of image restoration, particularly for image denoising and single image super-resolution. Deep learning technique…