Results 21 to 30 of about 39,632 (294)
Generative Adversarial Networks: A Primer for Radiologists
RadioGraphics, 2021 Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs).
Jelmer M. Wolterink +5 more
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Constrained Generative Adversarial Networks [PDF]
IEEE Access, 2021 Generative Adversarial Networks (GANs) are a powerful subclass of generative models. Yet, how to effectively train them to reach Nash equilibrium is a challenge. A number of experiments have indicated that one possible solution is to bound the function space of the discriminator.
Xiaopeng Chao +4 more
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Inverting the Generator of a Generative Adversarial Network [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Under review at IEEE ...
Antonia Creswell, Anil Anthony Bharath
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Triple Generative Adversarial Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax game consisting of a generator, a classifier and a discriminator, and therefore is referred to as Triple Generative Adversarial Network (Triple-GAN).
Chongxuan Li +4 more
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Fuzzy Generative Adversarial Networks
CoRR, 2021 Generative Adversarial Networks (GANs) are well-known tools for data generation and semi-supervised classification. GANs, with less labeled data, outperform Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) in classification across various tasks, this shows promise for developing GANs capable of trespassing into the domain of semi ...
Ryan Nguyen +2 more
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Regularized Generative Adversarial Network [PDF]
SSRN Electronic Journal, 2021 18 pages. Comments are welcome!
Gabriele Di Cerbo +2 more
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Intervention Generative Adversarial Networks
CoRR, 2020 In this paper we propose a novel approach for stabilizing the training process of Generative Adversarial Networks as well as alleviating the mode collapse problem. The main idea is to introduce a regularization term that we call intervention loss into the objective.
Jiadong Liang +3 more
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Noise robust chi-square generative adversarial network [PDF]
, 2020 Aiming at the obvious difference of image quality generated by generative adversarial network under different noises,a chi-square generative adversarial network (CSGAN) was proposed.Combing the advantages of quantification sensitivity and sparse ...
Hongjun LI, Shibing ZHANG, Chaobo LI
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A PRIMER ON GENERATIVE ADVERSARIAL NETWORKS
International Journal of Innovative Research in Computer Science & Technology, 2020 Generative Adversarial Networks (GANs) is a type of deep neural network architecture that utilizes unsupervised machine learning to generate data. They were presented in 2014, in a paper by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This paper will introduce the core components of GANs.
Dr. Vikas Thada +4 more
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Multi‐style Chinese art painting generation of flowers
IET Image Processing, 2021 With the proposal and development of Generative Adversarial Networks, the great achievements in the field of image generation are made. Meanwhile, many works related to the generation of painting art have also been derived. However, due to the difficulty
Feifei Fu +3 more
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