Results 11 to 20 of about 124,232 (320)
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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Optoelectronic generative adversarial networks
Communications PhysicsRecent breakthroughs in artificial intelligence generative content technology are driving transformational change. Diffractive optical networks offer a promising solution for high-speed, low-power generative models.
Jumin Qiu +5 more
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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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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 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 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 ...
Mauro, Giovanni +4 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.
Evgeny Burnaev +2 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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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 ...
Mingyan Liu +6 more
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Generative Adversarial Networks
International Journal for Research in Applied Science and Engineering Technology, 2021 Abstract: Deep learning's breakthrough in the field of artificial intelligence has resulted in the creation of a slew of deep learning models. One of these is the Generative Adversarial Network, which has only recently emerged. The goal of GAN is to use unsupervised learning to analyse the distribution of data and create more accurate results.
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