1 code implementation • 1 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.
1 code implementation • 15 Jan 2022 • Jiqing Wu, Nanda Horeweg, Marco de Bruyn, Remi A. Nout, Ina M. Jürgenliemk-Schulz, Ludy C. H. W. Lutgens, Jan J. Jobsen, Elzbieta M. van der Steen-Banasik, Hans W. Nijman, Vincent T. H. B. M. Smit, Tjalling Bosse, Carien L. Creutzberg, Viktor H. Koelzer
Randomized controlled trials (RCTs) are considered as the gold standard for testing causal hypotheses in the clinical domain.
no code implementations • 1 Jan 2021 • Jiqing Wu, Inti Zlobec, Viktor Kölzer
Causal visual discovery is a fundamental yet challenging problem in many research fields.
no code implementations • 23 Oct 2019 • Zhiwu Huang, Danda Pani Paudel, Guanju Li, Jiqing Wu, Radu Timofte, Luc van Gool
This paper introduces a divide-and-conquer inspired adversarial learning (DACAL) approach for photo enhancement.
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.
Ranked #1 on Video Generation on TrailerFaces
no code implementations • 5 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.
no code implementations • 4 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.
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.
1 code implementation • 8 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.
no code implementations • 5 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.
no code implementations • 17 Nov 2016 • Zhiwu Huang, Jiqing Wu, Luc van Gool
Learning representations on Grassmann manifolds is popular in quite a few visual recognition tasks.
no code implementations • 26 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.