Results 31 to 40 of about 219,883 (315)
ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2020 Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) is a perceptual-driven approach for single image super-resolution that is able to produce photorealistic images.
Nathanaël Carraz Rakotonirina +1 more
semanticscholar +1 more source
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
doaj +1 more source
Learning Universal Adversarial Perturbations with Generative Models [PDF]
, 2018 Neural networks are known to be vulnerable to adversarial examples, inputs that have been intentionally perturbed to remain visually similar to the source input, but cause a misclassification.
Danezis, George, Hayes, Jamie
core +2 more sources
Adversarial Examples Detection for XSS Attacks Based on Generative Adversarial Networks
IEEE Access, 2020 Models based on deep learning are prone to misjudging the results when faced with adversarial examples. In this paper, we propose an MCTS-T algorithm for generating adversarial examples of cross-site scripting (XSS) attacks based on Monte Carlo tree ...
Xueqin Zhang +4 more
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source
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
doaj +1 more source