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Virtual try-on with generative adversarial networks

, 2019
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f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks

Medical Image Analysis, 2019
Thomas Schlegl, Philipp Seeböck, Sebastian M Waldstein   +2 more
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Generative Adversarial Networks in Cardiology

Canadian Journal of Cardiology, 2022
Generative adversarial networks (GANs) are state-of-the-art neural network models used to synthesise images and other data. GANs brought a considerable improvement to the quality of synthetic data, quickly becoming the standard for data-generation tasks.
Skandarani, Youssef, Lalande, Alain, Afilalo, Jonathan, Jodoin, Pierre-Marc   +3 more
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ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

ECCV Workshops, 2018
The Super-Resolution Generative Adversarial Network (SRGAN) is a seminal work that is capable of generating realistic textures during single image super-resolution. However, the hallucinated details are often accompanied with unpleasant artifacts.
Xintao Wang, Ke Yu, Shixiang Wu, Jinjin Gu, Yihao Liu, Chao Dong, Chen Change Loy, Y. Qiao, Xiaoou Tang   +8 more
semanticscholar   +1 more source

Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Information Processing in Medical Imaging, 2017
Obtaining models that capture imaging markers relevant for disease progression and treatment monitoring is challenging. Models are typically based on large amounts of data with annotated examples of known markers aiming at automating detection.
T. Schlegl, Philipp Seeböck, S. Waldstein, U. Schmidt-Erfurth, G. Langs   +4 more
semanticscholar   +1 more source

MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks

International Conference on Artificial Neural Networks, 2019
The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of multivariate time series data for these systems.
Dan Li, Dacheng Chen, Lei Shi, Baihong Jin, Jonathan Goh, See-Kiong Ng   +5 more
semanticscholar   +1 more source

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

European Conference on Computer Vision, 2016
This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis ...
Chuan Li, Michael Wand
semanticscholar   +1 more source