Results 31 to 40 of about 16,046 (295)
Face Aging With Boundary Equilibrium Conditional Autoencoder
IEEE Access, 2018 Since generative adversarial networks (GANs) were proposed in 2014, mode collapse has been a problem that affects many researchers when training GANs. With the reconstruction loss of an autoencoder, conditional adversarial autoencoder (CAAE) is free from
Longxiang Chen +2 more
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Attention-Aware Generative Adversarial Networks (ATA-GANs) [PDF]
2018 IEEE 13th Image, Video, and Multidimensional Signal Processing Workshop (IVMSP), 2018 In this work, we present a novel approach for training Generative Adversarial Networks (GANs). Using the attention maps produced by a Teacher- Network we are able to improve the quality of the generated images as well as perform weakly object localization on the generated images.
Dimitris Kastaniotis +4 more
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Generative Adversarial Network (GAN) based Image-Deblurring
CoRR, 2022 90 pages, 35 figures, MS Thesis at the University of ...
Yuhong Lu, Nicholas Polydorides
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Guided generative adversarial neural network for representation learning and audio generation using fewer labelled audio data [PDF]
, 2021 The Generation power of Generative Adversarial Neural Networks (GANs) has shown great promise to learn representations from unlabelled data while guided by a small amount of labelled data.
Cummins, Nicholas +10 more
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IEEE Access, 2023 Pneumonia is an inflammation of the lungs caused by pathogens or autoimmune diseases, with about 450 million patients worldwide each year. Chest X–ray analysis is the most common radiographic method used to diagnose pneumonia, and advances in deep
Yeongbong Jin, Woojin Chang, Bonggyun Ko
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Multi-Output Regression with Generative Adversarial Networks (MOR-GANs) [PDF]
, 2022 Regression modelling has always been a key process in unlocking the relationships between independent and dependent variables that are held within data. In recent years, machine learning has uncovered new insights in many fields, providing predictions to
Chung, Kian Fan +15 more
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Creating Images with Stable Diffusion and Generative Adversarial Networks [PDF]
International Journal of TelecommunicationsIn this study, Generative Adversarial Networks (GANs) and Stable Diffusion represent two powerful methodologies in the field of generative models, with applications across image generation, creative design, and beyond. GANs consist of two neural networks,
mohamed sadek +3 more
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Multi-Stage Hybrid Text-to-Image Generation Models [PDF]
International Journal of Intelligent Computing and Information Sciences, 2022 Generative Adversarial Networks (GANs) have proven their outstanding potential in creating realistic images that can't differentiate between them and the real images, but text-to-image (conditional generation) still faces some challenges.
Razan Bayoumi +2 more
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Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential
Patterns, 2020 Summary: Image analysis in the field of digital pathology has recently gained increased popularity. The use of high-quality whole-slide scanners enables the fast acquisition of large amounts of image data, showing extensive context and microscopic detail
Maximilian E. Tschuchnig +2 more
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Geological facies modeling based on Generative Adversarial Networks (GANs)
, 2020 Geological facies modeling based on progressive growing of Generative Adversarial Networks ...
Suihong Song
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