Search Results for author: Jiqing Wu

Found 12 papers, 5 papers with code

Towards IID representation learning and its application on biomedical data

1 code implementation1 Mar 2022 Jiqing Wu, Inti Zlobec, Maxime Lafarge, Yukun He, Viktor H. Koelzer

Compared to the SOTA baselines supported in WILDS, the results confirm the superior performance of IID representation learning on OOD tasks.

Benchmarking Representation Learning

Sliced Wasserstein Generative Models

1 code implementation CVPR 2019 Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool

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

Image Generation Video Generation

Manifold-valued Image Generation with Wasserstein Generative Adversarial Nets

no code implementations5 Dec 2017 Zhiwu Huang, Jiqing Wu, Luc van Gool

In addition, we recommend three benchmark datasets that are CIFAR-10 HSV/CB color images, ImageNet HSV/CB color images, UCL DT image datasets.

Image Generation

Face Translation between Images and Videos using Identity-aware CycleGAN

no code implementations4 Dec 2017 Zhiwu Huang, Bernhard Kratzwald, Danda Pani Paudel, Jiqing Wu, Luc van Gool

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

Image-to-Image Translation Translation +1

Wasserstein Divergence for GANs

1 code implementation ECCV 2018 Jiqing Wu, Zhiwu Huang, Janine Thoma, Dinesh Acharya, Luc van Gool

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-of-the-art due to the theoretical contributions and competitive qualitative performance.

Image Generation

Sliced Wasserstein Generative Models

1 code implementation8 Jun 2017 Jiqing Wu, Zhiwu Huang, Dinesh Acharya, Wen Li, Janine Thoma, Danda Pani Paudel, Luc van Gool

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

Image Generation Video Generation

On the Relation between Color Image Denoising and Classification

no code implementations5 Apr 2017 Jiqing Wu, Radu Timofte, Zhiwu Huang, Luc van Gool

Inspired by classification models, we propose a novel deep learning architecture for color (multichannel) image denoising and report on thousands of images from ImageNet dataset as well as commonly used imagery.

Classification Color Image Denoising +3

Building Deep Networks on Grassmann Manifolds

no code implementations17 Nov 2016 Zhiwu Huang, Jiqing Wu, Luc van Gool

Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks.

Generic 3D Convolutional Fusion for image restoration

no code implementations26 Jul 2016 Jiqing Wu, Radu Timofte, Luc van Gool

Unlike other methods adapted to different tasks, our method uses the exact same convolutional network architecture to address both image denois- ing and single image super-resolution.

Image Denoising Image Restoration +1

Cannot find the paper you are looking for? You can Submit a new open access paper.