Results 31 to 40 of about 39,370 (305)
Regularized Generative Adversarial Network [PDF]
SSRN Electronic Journal, 2021 18 pages. Comments are welcome!
Gabriele Di Cerbo +2 more
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Higher-Resolution-and-Less-Noisy-Seismic-Images-An-Application-of-Generative-Adversarial-Neural-Net
, 2022 An application of generative adversarial networks to seismic data processing (resolution ehancement and denoising). This is a repository for the paper "Higher Resolution and Less Noisy Seismic Images: An Application of Generative Adversarial Neural Net" (
Lei Lin (12656614)
core +1 more source
Dynamics of Fourier Modes in Torus Generative Adversarial Networks
, 2021 Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution.
González Prieto, José Ángel +3 more
core +1 more source
Applied Sciences, 2023 Advancements in technology have improved human well-being but also enabled new avenues for criminal activities, including digital exploits like deep fakes, online fraud, and cyberbullying.
Claudio Navar Valdebenito Maturana +2 more
doaj +1 more source
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
openaire +2 more sources
Controllable Generative Adversarial Network [PDF]
IEEE Access, 2019 A fully revised version of this paper is published in IEEE Access.
Minhyeok Lee, Junhee Seok
openaire +3 more sources
Generative image inpainting for retinal images using generative adversarial networks [PDF]
, 2021 The diagnosis and treatment of eye diseases is heavily reliant on the availability of retinal imagining equipment. To increase accessibility, lower-cost ophthalmoscopes, such as the Arclight, have been developed.
Ognjen Arandjelovic +3 more
core +1 more source
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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A Brute-Force Black-Box Method to Attack Machine Learning-Based Systems in Cybersecurity
IEEE Access, 2020 Machine learning algorithms are widely utilized in cybersecurity. However, recent studies show that machine learning algorithms are vulnerable to adversarial examples.
Sicong Zhang, Xiaoyao Xie, Yang Xu
doaj +1 more source
Generative Adversarial Neural Networks for Denoising Coherent Multidimensional Spectra
, 2022 Ultrafast spectroscopy often involves measuring weak signals and long data acquisition times. Spectra are typically collected as a “pump-probe” spectrum by measuring differences in intensity across laser shots.
Ziareena, Al-Mualem, Carlos, Baiz
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