Results 31 to 40 of about 124,149 (320)
Regularized Generative Adversarial Network [PDF]
SSRN Electronic Journal, 2021 18 pages. Comments are welcome!
Di Cerbo, Gabriele +2 more
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Super‐resolution with adversarial loss on the feature maps of the generated high‐resolution image
Electronics Letters, 2022 Recent studies on image super‐resolution make use of Generative Adversarial Networks to generate the high‐resolution image counterpart of the low‐resolution input. However, while being able to generate sharp high‐resolution images, Generative Adversarial
I. Imanuel, S. Lee
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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.
Jyotsna Sharma +4 more
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Stacked Generative Adversarial Networks [PDF]
2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017 CVPR 2017, camera-ready ...
John E. Hopcroft +4 more
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Tomographic reconstruction with a generative adversarial network [PDF]
Journal of Synchrotron Radiation, 2020 This paper presents a deep learning algorithm for tomographic reconstruction (GANrec). The algorithm uses a generative adversarial network (GAN) to solve the inverse of the Radon transform directly. It works for independent sinograms without additional training steps.
Maik Kahnt +9 more
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Generative Adversarial Networks in finance: an overview [PDF]
SSRN Electronic Journal, 2021 Modelling in finance is a challenging task: the data often has complex statistical properties and its inner workings are largely unknown. Deep learning algorithms are making progress in the field of data-driven modelling, but the lack of sufficient data to train these models is currently holding back several new applications.
Joerg Osterrieder, Florian Eckerli
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Dairy Goat Image Generation Based on Improved-Self-Attention Generative Adversarial Networks
IEEE Access, 2020 The lack of long-range dependence in convolutional neural networks causes weaker performance in generative adversarial networks(GANs) with regard to generating image details. The self-attention generative adversarial network(SAGAN) use the self-attention
Huan Li, Jinglei Tang
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Exploring generative adversarial networks and adversarial training
International Journal of Cognitive Computing in Engineering, 2022 Recognized as a realistic image generator, Generative Adversarial Network (GAN) occupies a progressive section in deep learning. Using generative modeling, the underlying generator model learns the real target distribution and outputs fake samples from ...
Afia Sajeeda, B M Mainul Hossain, Ph.D
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Semi-supervised Learning on Graphs Using Adversarial Training with Generated Sample [PDF]
Jisuanji kexue yu tansuo, 2023 Given a graph composed of a small number of labeled nodes and a large number of unlabeled nodes, semi-supervised learning on graphs aims to assign labels for the unlabeled nodes.
WANG Cong, WANG Jie, LIU Quanming, LIANG Jiye
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The Bures Metric for Generative Adversarial Networks [PDF]
, 2021 Additional empirical ...
de Meulemeester, Hannes +4 more
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