Results 281 to 290 of about 39,632 (294)

A Hero's Journey to Systemic Change: Developing Expertise in Agricultural Development

Sustainable Development, EarlyView.
ABSTRACT Experts in rural development and agrarian change wield considerable influence over programme design and delivery, directing resources and shaping trajectories towards the Sustainable Development Goals (SDGs). Yet the pathways through which such expertise develops remain under‐examined. This study examines the journey to expertise among experts
Kirt Hainzer, Caitlin Finlayson, Nicholas Harrigan, Andrew McGregor, Katharine McKinnon, Van Touch, Ariane Utomo, Brian R. Cook   +7 more
wiley   +1 more source

Using multimodal PET+MR data as conditional generative adversarial network inputs improves pseudo-CT and attenuation correction estimates for brain PET/MR. [PDF]

Am J Nucl Med Mol Imaging
Fisher J, Anaya E, Chinn G, Levin CS.
europepmc   +1 more source

USSGAN: Unsupervised Spectral and Spatial Attention-Based Generative Adversarial Network for Cholangiocarcinoma Detection. [PDF]

Chem Biomed Imaging
Kumar SS, Deshpande A, Nair PA, Aala S, Chinnadurai S, Dodda VC, Muniraj I, Sarker MAL, Mostafa H.   +8 more
europepmc   +1 more source

Dosimetric evaluations using cycle-consistent generative adversarial network synthetic CT for MR-guided adaptive radiation therapy. [PDF]

Front Oncol
Asher GL, Wang S, Zaki BI, Russo GA, Gill GS, Thomas CR, Prioleau TO, Li Y, Zhang R, Yan Y, Hunt B.   +10 more
europepmc   +1 more source

AIR-GANet: multi-head attention integrated residual dense block based generative adversarial network for visible and infrared image fusion. [PDF]

Sci Rep
Bineeshia J, Kumar BV.
europepmc   +1 more source

<i>Gliomap-GAN</i>: A conditional generative adversarial network to visualize glioblastoma's cell density from contrast-enhanced magnetic resonance imaging. [PDF]

Neurooncol Adv
Kinoshita M, Miyake K, Ide W, Arita H, Isohashi K, Hatazawa J, Kishima H.   +6 more
europepmc   +1 more source

Map geographic information road extraction method based on generative adversarial network and U-Net. [PDF]

Sci Rep
Liu G, He H, Gao Y, Zhang G, Cao T.
europepmc   +1 more source

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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   +1 more source

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

Robust generative adversarial network

Machine Learning, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Shufei Zhang, Zhuang Qian, Kaizhu Huang, Rui Zhang 0012, Jimin Xiao, Yuan He, Canyi Lu   +6 more
openaire   +2 more sources