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

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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Bibliographic Details
Main Author: Wasi Ahmad (23124064) (author)
Other Authors: Md. Faysal Ahamed (21842396) (author), Amith Khandakar (14151981) (author), SM Ashfaq Uz Zaman (23124067) (author), Mohamed Arselene Ayari (17873878) (author)
Published: 2025
Subjects:
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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