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Generative Adversarial Networks in Cardiology
Canadian Journal of Cardiology, 2022Generative 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 +3 more
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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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Convolutional Generative Adversarial Networks
2023El objetivo de este trabajo es implementar tres modelos de redes GAN diferentes, como son la DCGAN, WGAN y WGAN-GP, para generar imágenes similares a las que conforman los datasets de MNIST y de CelebA. Para cada uno de los tres modelos se realizan cuatro entrenamientos, utilizando diferentes números de imágenes de los dos datasets para obtener una ...
Sánchez Hernández, Sergi +1 more
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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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Generative Adversarial Networks in Time Series: A Systematic Literature Review
ACM Computing Surveys, 2023Eoin Brophy, Zhengwei Wang, Qi She
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A Survey on Generative Adversarial Networks: Variants, Applications, and Training
ACM Computing Surveys, 2022Songyuan Li
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