Results 21 to 30 of about 45,910 (264)
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Generative adversarial networks (GANs) have achieved many excellent results in hyperspectral image (HSI) classification in recent years, as GANs can effectively solve the dilemma of limited training samples in HSI classification.
Ziping He +5 more
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Generative Adversarial Network (GAN) to Generate Realistic Images
International Journal for Research in Applied Science and Engineering Technology, 2023 Abstract: Generative Adversarial Networks (GANs) have rapidly become a focal point of research due to their ability to generate realistic images. First introduced in 2014, GANs have been applied in a multitude of fields such as computer vision and natural language processing, yielding impressive results.
Sahil Lamba +3 more
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Pun-GAN: Generative Adversarial Network for Pun Generation [PDF]
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), 2019 En este trabajo, nos centramos en la tarea de generar una oración de juego de palabras dado un par de sentidos de la palabra. Un desafío importante para la generación de juegos de palabras es la falta de un corpus de juegos de palabras a gran escala para guiar el aprendizaje supervisado. Para remediar esto, proponemos una red generativa adversaria para
Fuli Luo +6 more
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Survey on Generative Adversarial Behavior in Artificial Neural Tasks
Iraqi Journal for Computer Science and Mathematics, 2022 GANs (generative opposing networks) are a technique for learning deep representations in the absence of a large amount of annotated training data. This is accomplished through the use of a competitive technique that employs two networks to generate ...
Roheen Qamar +3 more
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Building Footprint Generation Using Improved Generative Adversarial Networks [PDF]
, 2018 Building footprint information is an essential ingredient for 3-D reconstruction of urban models. The automatic generation of building footprints from satellite images presents a considerable challenge due to the complexity of building shapes.
Li, Qingyu, Shi, Yilei, Zhu, Xiao Xiang
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Survey on Research Progress of Generating Adversarial Networks
Jisuanji kexue yu tansuo, 2020 Since the birth of generative adversarial networks (GANs), the research on it has become a hot spot in the field of machine learning. It uses the mechanism of adversarial learning to train model solving the problem that the generation algorithm cannot ...
WU Shaoqian, LI Ximing
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Quantum generative adversarial learning [PDF]
, 2018 Generative adversarial networks (GANs) represent a powerful tool for classical machine learning: a generator tries to create statistics for data that mimics those of a true data set, while a discriminator tries to discriminate between the true and fake ...
Lloyd, Seth, Weedbrook, Christian
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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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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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House-GAN++: Generative Adversarial Layout Refinement Networks
, 2021 This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement.
Nauata, Nelson +5 more
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