Results 111 to 120 of about 2,921,969 (298)

Triangle Generative Adversarial Networks

CoRR, 2017
A Triangle Generative Adversarial Network ($Δ$-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain correspondence is provided by only a few paired samples.
Zhe Gan, Liqun Chen 0001, Weiyao Wang 0002, Yunchen Pu, Yizhe Zhang 0002, Hao Liu 0015, Chunyuan Li, Lawrence Carin   +7 more
openaire   +3 more sources

Structural Divergence Between the Moltbook AI‐Agent Network and Human Social Networks

Advanced Science, EarlyView.
Analysis of the Moltbook AI‐agent network reveals a striking combination of familiar global scaling and distinct internal organization. Attention is highly concentrated, reciprocity is limited, connected triads are suppressed, and communities are strongly modular.
Wenpin Hou, Zhicheng Ji
wiley   +1 more source

Generative Adversarial Networks and Applications in Bioinformatics

, 2021
Generative Adversarial Networks (GAN) are currently considered a state-of-the-art method for image generation. Recently, Deep Convolutional Generative Adversarial Networks (DCGAN) yielded promising results in protein contact maps generation.
KOLESNICHENKO, Nikita
core  

Physics Informed Generative Surrogate Learning for Full Wave Validated Real Time Beamforming in Programmable Metasurfaces

Advanced Electronic Materials, EarlyView.
A physics‐guided generative surrogate framework is developed for programmable metasurface beamforming. Mode‐conditioned binary state generation, aperture‐physics prediction, routed residual correction, NSGA‐II optimization, and CST validation are combined to support fast candidate screening and full‐wave beam refinement across single‐beam, dual‐beam ...
Wenqian Liu, Weidong Chen, Chang Chen, Xiang Zhang, Sensong An   +4 more
wiley   +1 more source

AECT-GAN: reconstructing CT from biplane radiographs using auto-encoding generative adversarial networks

Cormputed tomography (CT) scanning is an effective medical imaging modality widely used in clinical medicine for diagnosing various conditions. CT can generate three-dimensional images, thus providing more information than traditional two-dimensional ...
Alike, Y, Su, K, Li, M, Chen, Q, Zhang, Q, Cheng, S, Wen, P   +6 more
core   +1 more source

Tympanic Membrane Generation with Generative Adversarial Networks

, 2021
© 2021 IEEE.Obtaining sufficient original data in most studies in the field of medical pattern recognition is a difficult and time consuming process. Different data augmentation methods are used to increase the amount of data to be used to train these ...
M. Elif Karsligil, Mustafa Furkan Eseoglu, Kocak, Ismail, KARSLIGİL YAVUZ, Mine Elif, Ismail Kocak, Eseoglu, Mustafa Furkan   +5 more
core   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Rapid measurements and phase transition detections made simple by AC-GANs

SciPost Physics Core
In recent years, significant attention has been paid to using end-to-end neural networks for analyzing Monte Carlo data. However, the exploration of non-end-to-end generative adversarial neural networks remains limited.
Jiewei Ding, Ho-Kin Tang, Wing Chi Yu
doaj   +1 more source

Coupled Generative Adversarial Networks

CoRR, 2016
We propose coupled generative adversarial network (CoGAN) for learning a joint distribution of multi-domain images. In contrast to the existing approaches, which require tuples of corresponding images in different domains in the training set, CoGAN can learn a joint distribution without any tuple of corresponding images.
Ming-Yu Liu 0001, Oncel Tuzel
openaire   +3 more sources

From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

Advanced Energy Materials, EarlyView.
The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho, Siwar Ben Hadj Ali, Maxence Desnoyers, Francisco Fernandez, Nassima Benammar, Alejandro A. Franco   +5 more
wiley   +1 more source