Results 11 to 20 of about 39,632 (294)

Controllable Generative Adversarial Network [PDF]

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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Lung image segmentation via generative adversarial networks [PDF]

Frontiers in Physiology
IntroductionLung 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, Hongfeng Zhu, Siyu Liu, Yang Qi, Rongshang Chen   +4 more
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Missing Data Imputation Method Combining Random Forest and Generative Adversarial Imputation Network

Sensors
(1) Background: In order to solve the problem of missing time-series data due to the influence of the acquisition system or external factors, a missing time-series data interpolation method based on random forest and a generative adversarial ...
Hongsen Ou, Yunan Yao, Yi He
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Generative Adversarial Networks

2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), 2023
Generative Adversarial Networks (GANs) are very popular frameworks for generating high-quality data, and are immensely used in both the academia and industry in many domains. Arguably, their most substantial impact has been in the area of computer vision, where they achieve state-of-the-art image generation.
Gilad Cohen, Raja Giryes
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Generative adversarial networks [PDF]

Communications of the ACM, 2020
Generative adversarial networks are a kind of artificial intelligence algorithm designed to solve the generative modeling problem. The goal of a generative model is to study a collection of training examples and learn the probability distribution that generated them.
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron C. Courville, Yoshua Bengio   +7 more
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Quaternion Generative Adversarial Networks [PDF]

, 2022
Accepted as a Chapter for the SPRINGER book "Generative Adversarial Learning: Architectures and Applications"
Grassucci, Eleonora, Cicero, Edoardo, Comminiello, Danilo   +2 more
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Steganographic generative adversarial networks [PDF]

Twelfth International Conference on Machine Vision (ICMV 2019), 2020
Steganography is collection of methods to hide secret information ("payload") within non-secret information "container"). Its counterpart, Steganalysis, is the practice of determining if a message contains a hidden payload, and recovering it if possible. Presence of hidden payloads is typically detected by a binary classifier.
Denis Volkhonskiy, Ivan Nazarov, Evgeny Burnaev   +2 more
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Guided generative adversarial neural network for representation learning and audio generation using fewer labelled audio data [PDF]

, 2021
The Generation power of Generative Adversarial Neural Networks (GANs) has shown great promise to learn representations from unlabelled data while guided by a small amount of labelled data.
Cummins, Nicholas, Rana, Rajib, Busso, Carlos, Hansen, John H.L., Hansen, John H. L., Schuller, Bjoern W., Haque, Kazi Nazmul, Hansen, John, Schuller, Bjorn W., Liu, Jiajun, Schuller, Björn   +10 more
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Generating mobility networks with generative adversarial networks

EPJ Data Science, 2022
AbstractThe increasingly crucial role of human displacements in complex societal phenomena, such as traffic congestion, segregation, and the diffusion of epidemics, is attracting the interest of scientists from several disciplines. In this article, we address mobility network generation, i.e., generating a city’s entire mobility network, a weighted ...
Giovanni Mauro, Massimiliano Luca, Antonio Longa, Bruno Lepri, Luca Pappalardo   +4 more
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Generating Adversarial Examples with Adversarial Networks [PDF]

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018
Deep neural networks (DNNs) have been found to be vulnerable to adversarial examples resulting from adding small-magnitude perturbations to inputs. Such adversarial examples can mislead DNNs to produce adversary-selected results. Different attack strategies have been proposed to generate adversarial examples, but how to produce them with high ...
Chaowei Xiao, Bo Li 0026, Jun-Yan Zhu, Warren He, Mingyan Liu, Dawn Song   +5 more
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