Results 21 to 30 of about 2,921,969 (298)
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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Generative Adversarial Neural Networks for Denoising Coherent Multidimensional Spectra [PDF]
, 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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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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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
Score-Guided Generative Adversarial Networks
Axioms, 2022 We propose a generative adversarial network (GAN) that introduces an evaluator module using pretrained networks. The proposed model, called a score-guided GAN (ScoreGAN), is trained using an evaluation metric for GANs, i.e., the Inception score, as a ...
Minhyeok Lee, Junhee Seok
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Numerical Analysis of Bio-signal Using Generative Adversarial Networks [PDF]
, 2020 In this decade, it is not necessary to have technical knowledge for the investment since the automatic algorithms to sell/buy investment destination have been developed with artificial intelligence (AI).
Ono, Rentarou +9 more
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Geometric Morphometric Data Augmentation Using Generative Computational Learning Algorithms
Applied Sciences, 2020 The fossil record is notorious for being incomplete and distorted, frequently conditioning the type of knowledge that can be extracted from it. In many cases, this often leads to issues when performing complex statistical analyses, such as classification
Lloyd A. Courtenay +1 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)
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