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Constrained Generative Adversarial Networks [PDF]
IEEE Access, 2021 Generative Adversarial Networks (GANs) are a powerful subclass of generative models. Yet, how to effectively train them to reach Nash equilibrium is a challenge.
Xiaopeng Chao +4 more
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Steganographic Generative Adversarial Networks [PDF]
Twelfth International Conference on Machine Vision (ICMV 2019), 2019 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.
Burnaev, Evgeny +2 more
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Generating mobility networks with generative adversarial networks
EPJ Data Science, 2022 The 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.
Giovanni Mauro +4 more
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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 +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 +2 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 +5 more
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Generative Adversarial Networks: A Primer for Radiologists
RadioGraphics, 2021 Artificial intelligence techniques involving the use of artificial neural networks-that is, deep learning techniques-are expected to have a major effect on radiology. Some of the most exciting applications of deep learning in radiology make use of generative adversarial networks (GANs).
Jelmer M. Wolterink +5 more
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Inverting the Generator of a Generative Adversarial Network [PDF]
IEEE Transactions on Neural Networks and Learning Systems, 2019 Under review at IEEE ...
Antonia Creswell, Anil Anthony Bharath
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Triple Generative Adversarial Networks [PDF]
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022 We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax game consisting of a generator, a classifier and a discriminator, and therefore is referred to as Triple Generative Adversarial Network (Triple-GAN).
Chongxuan Li +4 more
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