CGE-GAN: Contrastive-guided evolutionary generative adversarial networks with dynamic adaptive weight sharing.

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Bibliographic Details
Title: CGE-GAN: Contrastive-guided evolutionary generative adversarial networks with dynamic adaptive weight sharing.
Authors: Iqbal K; School of Computer, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China. Electronic address: kashif.iqbal@nuist.edu.cn., Yu X; School of Software, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China. Electronic address: xueyu@nuist.edu.cn., Rafique A; School of Computer, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China. Electronic address: atifa.rafiquel@nuist.edu.cn., Hamid M; School of Mathematics and Statistics, Nanjing University of Information Science and Technology, No.219, Ningliu Road, Nanjing, 210044, Jiangsu, China. Electronic address: mhamid@nuist.edu.cn., Aurangzeb K; Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh, 11543, Saudi Arabia. Electronic address: kaurangzeb@ksu.edu.sa.
Source: Neural networks : the official journal of the International Neural Network Society [Neural Netw] 2026 Jul; Vol. 199, pp. 108702. Date of Electronic Publication: 2026 Feb 09.
Publication Type: Journal Article
Journal Info: Publisher: Pergamon Press Country of Publication: United States NLM ID: 8805018 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1879-2782 (Electronic) Linking ISSN: 08936080 NLM ISO Abbreviation: Neural Netw Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1879-2782
DOI:10.1016/j.neunet.2026.108702