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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2025
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