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MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
International Conference on Artificial Neural Networks, 2019The 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 +5 more
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Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks
European Conference on Computer Vision, 2016This 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
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Generative Adversarial Networks
2018For many AI projects, deep learning techniques are increasingly being used as the building blocks for innovative solutions ranging from image classification to object detection, image segmentation, image similarity, and text analytics (e.g., sentiment analysis, key phrase extraction). GANs, first introduced by Goodfellow et al.
Mathew Salvaris +2 more
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Generative Adversarial Network
2020Generative adversarial networks (GANs) are a type of deep learning model designed by Ian Goodfellow and his colleagues in 2014.
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Adversarial Machine Learning in Wireless Communications Using RF Data: A Review
IEEE Communications Surveys and Tutorials, 2023Damilola Adesina +2 more
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A Survey on Generative Adversarial Networks: Variants, Applications, and Training
ACM Computing Surveys, 2022Songyuan Li
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Generative Adversarial Networks in Time Series: A Systematic Literature Review
ACM Computing Surveys, 2023Eoin Brophy, Zhengwei Wang, Qi She
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