Results 31 to 40 of about 43,961 (294)
International Journal of Digital Multimedia Broadcasting, 2021 The convolutional neural network has achieved good results in the superresolution reconstruction of single-frame images. However, due to the shortcomings of infrared images such as lack of details, poor contrast, and blurred edges, superresolution ...
Yuqing Zhao +4 more
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Survey of generative adversarial network
网络与信息安全学报, 2021 Firstly, the basic theory, application scenarios and current state of research of GAN (generative adversarial network) were introduced, and the problems need to be improved were listed. Then, recent research, improvement mechanism and model features in 2
WANG Zhenglong, ZHANG Baowen
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Improved Wasserstein conditional generative adversarial network speech enhancement
EURASIP Journal on Wireless Communications and Networking, 2018 The speech enhancement based on the generative adversarial network has achieved excellent results with large quantities of data, but performance in the low-data regime and tasks like unseen data learning still lag behind.
Shan Qin, Ting Jiang
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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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Controllable Generative Adversarial Network
IEEE Access, 2019 Recently introduced generative adversarial networks (GANs) have been shown numerous promising results to generate realistic samples. In the last couple of years, it has been studied to control features in synthetic samples generated by the GAN. Auxiliary
Minhyeok Lee, Junhee Seok
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Компьютерная оптикаSingle-pixel imaging is a promising image acquisition method that provides an alternative to traditional imaging methods using multi-pixel matrices. However, algorithmic image reconstruction from measurements of a single-pixel camera is a non-trivial ...
D.V. Babukhin, A.A. Reutov, D.V. Sych
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Self-Sparse Generative Adversarial Networks
CAAI Artificial Intelligence Research, 2022 Generative Adversarial Networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the high dimensional parameter space and the zero gradient problem.
Wenliang Qian +3 more
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Generative Adversarial Optical Networks Using Diffractive Layers for Digit and Action Generation
PhotonicsWithin the traditional electronic neural network framework, Generative Adversarial Networks (GANs) have achieved extensive applications across multiple domains, including image synthesis, style transfer and data augmentation.
Pei Hu +3 more
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Entangling Quantum Generative Adversarial Networks
Physical Review Letters, 2022 Generative adversarial networks (GANs) are one of the most widely adopted semisupervised and unsupervised machine learning methods for high-definition image, video, and audio generation. In this work, we propose a new type of architecture for quantum generative adversarial networks (entangling quantum GAN, EQ-GAN) that overcomes some limitations of ...
Murphy Yuezhen Niu +6 more
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Lung image segmentation via generative adversarial networks
Frontiers in PhysiologyIntroductionLung image segmentation plays an important role in computer-aid pulmonary disease diagnosis and treatment.MethodsThis paper explores the lung CT image segmentation method by generative adversarial networks.
Jiaxin Cai +4 more
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