Results 11 to 20 of about 2,911,065 (235)
Dynamics of Fourier Modes in Torus Generative Adversarial Networks
Mathematics, 2021 Generative Adversarial Networks (GANs) are powerful machine learning models capable of generating fully synthetic samples of a desired phenomenon with a high resolution.
Ángel González-Prieto +3 more
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Generative Adversarial Networks (GANs) [PDF]
ACM Computing Surveys, 2021 Generative Adversarial Networks (GANs) is a novel class of deep generative models that has recently gained significant attention. GANs learn complex and high-dimensional distributions implicitly over images, audio, and data. However, there exist major challenges in training of GANs, i.e., mode collapse, non-convergence, and instability, due to ...
Divya Saxena, Jiannong Cao 0001
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Games of GANs: Game-Theoretical Models for Generative Adversarial Networks
Artificial Intelligence Review, 2022 Abstract Generative Adversarial Networks (GANs) have recently attracted considerable attention in the AI community due to their ability to generate high-quality data of significant statistical resemblance to real data. Fundamentally, GAN is a game between two neural networks trained in an adversarial manner to reach a zero-sum Nash equilibrium ...
Monireh Mohebbi Moghaddam +6 more
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Geological facies modeling based on Generative Adversarial Networks (GANs)
, 2020 Geological facies modeling based on progressive growing of Generative Adversarial Networks ...
Suihong Song
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Meta-learning Enabled Score-Based Generative Adversarial Networks (GANs)
International Journal of Computational Intelligence SystemsThe capacity of Generative Adversarial Networks (GANs) to provide high-quality data has led to their significant attention. On the other hand, hyperparameter adjustment is a common part of GAN training, which may increase computing costs and result in ...
P. Navaneethakrishnan +3 more
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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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I-GANs for Infrared Image Generation
Complexity, 2021 The making of infrared templates is of great significance for improving the accuracy and precision of infrared imaging guidance. However, collecting infrared images from fields is difficult, of high cost, and time-consuming.
Bing Li +4 more
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Dairy Goat Image Generation Based on Improved-Self-Attention Generative Adversarial Networks
IEEE Access, 2020 The lack of long-range dependence in convolutional neural networks causes weaker performance in generative adversarial networks(GANs) with regard to generating image details. The self-attention generative adversarial network(SAGAN) use the self-attention
Huan Li, Jinglei Tang
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Quaternion Generative Adversarial Networks [PDF]
, 2021 Latest Generative Adversarial Networks (GANs) are gathering outstanding results through a large-scale training, thus employing models composed of millions of parameters requiring extensive computational capabilities.
Grassucci, Eleonora +2 more
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Generative adversarial networks and diffusion models in material discovery [PDF]
, 2023 The idea of materials discovery has excited and perplexed research scientists for centuries. Several different methods have been employed to find new types of materials, ranging from the arbitrary replacement of atoms in a crystal structure to advanced ...
Michael, Alverson +5 more
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