Results 21 to 30 of about 2,911,065 (235)
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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LDDMM Meets GANs: Generative Adversarial Networks for Diffeomorphic Registration
Jornada de Jóvenes Investigadores del I3A, 2021 In this work, we propose an unsupervised adversarial learning LDDMM method for 3D mono-modal images based on Generative Adversarial Networks. We have successfully implemented two models with stationary and EPDiff constrained non-stationary parameterizations of diffeomorphisms.
Ubaldo Ramon +2 more
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PEGANs: Phased Evolutionary Generative Adversarial Networks with Self-Attention Module
Mathematics, 2022 Generative adversarial networks have made remarkable achievements in generative tasks. However, instability and mode collapse are still frequent problems.
Yu Xue +3 more
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PFA-GAN: Progressive Face Aging With Generative Adversarial Network [PDF]
IEEE Transactions on Information Forensics and Security, 2021 Face aging is to render a given face to predict its future appearance, which plays an important role in the information forensics and security field as the appearance of the face typically varies with age. Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically ...
Zhizhong Huang +3 more
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Numerical Analysis of Bio-signal Using Generative Adversarial Networks [PDF]
, 2020 In this decade, it is not necessary to have technical knowledge for the investment since the automatic algorithms to sell/buy investment destination have been developed with artificial intelligence (AI).
Ono, Rentarou +9 more
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Lifelong Twin Generative Adversarial Networks [PDF]
, 2021 In this paper, we propose a new continuously learning generative model, called the Lifelong Twin Generative Adversarial Networks (LT-GANs). LT-GANs learns a sequence of tasks from several databases and its architecture consists of three components: two ...
Ye, Fei, Bors, Adrian Gheorghe
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Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)
Signals, 2021 Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision, in particular. Compared to such examples, however, there have been more limited applications of GANs to
Jaleh Zand, Stephen Roberts
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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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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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Generative Adversarial Network for Medical Images (MI-GAN) [PDF]
Journal of Medical Systems, 2018 Deep learning algorithms produces state-of-the-art results for different machine learning and computer vision tasks. To perform well on a given task, these algorithms require large dataset for training. However, deep learning algorithms lack generalization and suffer from over-fitting whenever trained on small dataset, especially when one is dealing ...
Talha Iqbal, Hazrat Ali
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