Results 271 to 280 of about 4,829,196 (302)

Clinical Application of Using Diffusion-Based Wasserstein Generative Adversarial Network for Morphologic Analysis of Blood Cells. [PDF]

J Clin Lab Anal
Kim HY, Ngasa EE, Kim HJ, Kim B, Lim G, Park CH, Kwon HJ, Jang MA, Woo J.   +8 more
europepmc   +1 more source

A Multi-Property Optimizing Generative Adversarial Network for de novo Antimicrobial Peptide Design. [PDF]

Adv Sci (Weinh)
Liu J, Cui T, Wang T, Lin L, Zeng X, Lu D, Jiao S, Wang J, Li X, Xiao S, Xie D, Wang X, Wang Y, Shang X, Niu Y, Wei Z, Peng J.   +16 more
europepmc   +1 more source

RETRACTED: Super-Resolution pedestrian re-identification method based on bidirectional generative adversarial network.

PLoS One
Wang Y, Wu Y.
europepmc   +1 more source

Correction: Parameter-optimized generative adversarial network framework for synthetic MRI generation: fine-tuning critical variables for enhanced image fidelity. [PDF]

Front Med (Lausanne)
Arockia Selvarathinam ALXR, Anbalagan N, Srinivasu PN, Choi J, Ijaz MF.   +4 more
europepmc   +1 more source

scGACL: a generative adversarial network with multi-scale contrastive learning for accurate single-cell RNA sequencing imputation. [PDF]

Brief Bioinform
Jiang Y, Zhao M, Yan J, Tang J, Guo F.
europepmc   +1 more source

Generative Adversarial Networks: An Overview [PDF]

IEEE Signal Processing Magazine, 2018
Generative adversarial networks (GANs) provide a way to learn deep representations without extensively annotated training data. They achieve this through deriving backpropagation signals through a competitive process involving a pair of networks. The representations that can be learned by GANs may be used in a variety of applications, including image ...
Anil Bharath, Kailash Arulkumaran, Antonia Creswell   +2 more
exaly   +5 more sources

Evolutionary Generative Adversarial Networks [PDF]

IEEE Transactions on Evolutionary Computation, 2019
14 pages, 8 ...
Dacheng Tao, Xin Yao, Chang Xu
exaly   +3 more sources

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Interpretable Generative Adversarial Networks

Proceedings of the AAAI Conference on Artificial Intelligence, 2022
Learning a disentangled representation is still a challenge in the field of the interpretability of generative adversarial networks (GANs). This paper proposes a generic method to modify a traditional GAN into an interpretable GAN, which ensures that filters in an intermediate layer of the generator encode disentangled localized visual concepts.
Chao Li 0028, Kelu Yao, Jin Wang 0039, Boyu Diao, Yongjun Xu 0001, Quanshi Zhang   +5 more
openaire   +2 more sources

Generative Adversarial Networks in Cardiology

Canadian Journal of Cardiology, 2022
Generative adversarial networks (GANs) are state-of-the-art neural network models used to synthesise images and other data. GANs brought a considerable improvement to the quality of synthetic data, quickly becoming the standard for data-generation tasks.
Skandarani, Youssef, Lalande, Alain, Afilalo, Jonathan, Jodoin, Pierre-Marc   +3 more
openaire   +3 more sources

Generative Adversarial Networks for Classification

2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), 2017
Our team is reviewing tools and techniques that enable rapid prototyping. Generative Adversarial Networks (GANs) have been shown to reduce training requirements for detection problems. GANs compete generative and discriminative classifiers to improve detection performance.
Steven A. Israel, J. H. Goldstein, Jeffrey S. Klein, James Talamonti, Franklin Tanner, Shane Zabel, Philip A. Sallee, Lisa McCoy   +7 more
openaire   +2 more sources