UGA-GAN: Unified Geometry-Aware GAN for Enhanced Training and Generation of High-Dimensional Data

<p dir="ltr">Generative Adversarial Networks (GANs) have shown impressive performance in generating realistic data across various domains. However, they suffer from key challenges such as mode collapse, latent space disorganization, and geometric inconsistency, which hinder the gener...

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التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Wasi Ahmad (23124064) (author)
مؤلفون آخرون:	Md. Faysal Ahamed (21842396) (author), Amith Khandakar (14151981) (author), SM Ashfaq Uz Zaman (23124067) (author), Mohamed Arselene Ayari (17873878) (author)
منشور في:	2025
الموضوعات:	Information and computing sciences Artificial intelligence Machine learning Manifold learning latent space regularization mode collapse geometry aware GAN Generative adversarial networks Training Generators Videos Geometry High dimensional data Shape Data collection
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UGA-GAN: Unified Geometry-Aware GAN for Enhanced Training and Generation of High-Dimensional Data

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