Results 21 to 30 of about 3,042,897 (294)
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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Augmenting Generative Adversarial Networks for Speech Emotion Recognition [PDF]
, 2020 Generative adversarial networks (GANs) have shown potential in learning emotional attributes and generating new data samples. However, their performance is usually hindered by the unavailability of larger speech emotion recognition (SER) data.
Raja Jurdak +11 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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Improving Generative Adversarial Networks with Image Quality Assessment
, 2021 The research to find new ways to improve Generative Adversarial Networks (GANs) and ways to evaluate the data they produce is quite active. However, approaches to directly using those evaluation steps to improve Generative Adversarial Networks are quite ...
Perkins-Ollila, Justin W.
core +4 more sources
BoostNet: A Boosted Convolutional Neural Network for Image Blind Denoising
IEEE Access, 2021 Deep convolutional neural networks and generative adversarial networks currently attracted the attention of researchers because it is more effective than conventional representation-based methods.
Duc My Vo +3 more
doaj +1 more source
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
Quantum generative adversarial networks [PDF]
Physical Review A, 2018 10 pages, 8 ...
Pierre-Luc Dallaire-Demers +1 more
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Fuzzy Generative Adversarial Networks
CoRR, 2021 Generative Adversarial Networks (GANs) are well-known tools for data generation and semi-supervised classification. GANs, with less labeled data, outperform Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) in classification across various tasks, this shows promise for developing GANs capable of trespassing into the domain of semi ...
Ryan Nguyen +2 more
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Self-Sparse Generative Adversarial Networks
CAAI Artificial Intelligence Research, 2022 Generative adversarial networks (GANs) are an unsupervised generative model that learns data distribution through adversarial training. However, recent experiments indicated that GANs are difficult to train due to the requirement of optimization in the ...
Wenliang Qian +3 more
doaj +1 more source