Results 21 to 30 of about 16,046 (295)
Quaternion Generative Adversarial Networks
, 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 (GAN)
This paper presents a comprehensive study on Generative Adversarial Networks (GANs), a powerful deep learning technique for generating realistic synthetic data. The work focuses on understanding the core architecture of GANs, which consists of two competing neural networks—the generator and the discriminator—trained through an adversarial learning ...
Anjana Raju, Shamas P M, Sheena K M
+7 more sources
Generative adversarial networks and diffusion models in material discovery
, 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
core +1 more source
PEGANs: Phased Evolutionary Generative Adversarial Networks with Self-Attention Module
Mathematics, 2022 Generative adversarial networks have made remarkable achievements in generative tasks. However, instability and mode collapse are still frequent problems.
Yu Xue +3 more
doaj +1 more source
Mixture Density Conditional Generative Adversarial Network Models (MD-CGAN)
Signals, 2021 Generative Adversarial Networks (GANs) have gained significant attention in recent years, with impressive applications highlighted in computer vision, in particular. Compared to such examples, however, there have been more limited applications of GANs to
Jaleh Zand, Stephen Roberts
doaj +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022 Generative adversarial networks (GANs) have achieved many excellent results in hyperspectral image (HSI) classification in recent years, as GANs can effectively solve the dilemma of limited training samples in HSI classification.
Ziping He +5 more
doaj +1 more source
House-GAN++: Generative Adversarial Layout Refinement Networks
CoRR, 2021 This paper proposes a novel generative adversarial layout refinement network for automated floorplan generation. Our architecture is an integration of a graph-constrained relational GAN and a conditional GAN, where a previously generated layout becomes the next input constraint, enabling iterative refinement.
Nelson Nauata +5 more
openaire +2 more sources
PFA-GAN: Progressive Face Aging With Generative Adversarial Network [PDF]
IEEE Transactions on Information Forensics and Security, 2021 Face aging is to render a given face to predict its future appearance, which plays an important role in the information forensics and security field as the appearance of the face typically varies with age. Although impressive results have been achieved with conditional generative adversarial networks (cGANs), the existing cGANs-based methods typically ...
Zhizhong Huang +3 more
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Survey on Research Progress of Generating Adversarial Networks
Jisuanji kexue yu tansuo, 2020 Since the birth of generative adversarial networks (GANs), the research on it has become a hot spot in the field of machine learning. It uses the mechanism of adversarial learning to train model solving the problem that the generation algorithm cannot ...
WU Shaoqian, LI Ximing
doaj +1 more source
Survey on Generative Adversarial Behavior in Artificial Neural Tasks
Iraqi Journal for Computer Science and Mathematics, 2022 GANs (generative opposing networks) are a technique for learning deep representations in the absence of a large amount of annotated training data. This is accomplished through the use of a competitive technique that employs two networks to generate ...
Roheen Qamar +3 more
doaj +1 more source