Results 31 to 40 of about 134,465 (305)
Adversarial Gaussian Denoiser for Multiple-Level Image Denoising
Sensors, 2021 Image denoising is a challenging task that is essential in numerous computer vision and image processing problems. This study proposes and applies a generative adversarial network-based image denoising training architecture to multiple-level Gaussian ...
Aamir Khan +4 more
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Sensors, 2020 Time series classification and forecasting have long been studied with the traditional statistical methods. Recently, deep learning achieved remarkable successes in areas such as image, text, video, audio processing, etc.
Kun Zhou +3 more
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Implementasi Steganografi Gambar Menggunakan Algoritma Generative Adversarial Network
SINTECH (Science and Information Technology) Journal, 2023 In the era of information technology, it is very important to protect data and information so that irresponsible parties do not misuse it. One technique for securing data is steganography. Steganography is a technique of hiding messages in a medium.
Khairunnisak Khairunnisak +2 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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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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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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