1 Assistant Professor of Department of CSE(AI&ML) of ACE Engineering College.
2 Students of Department CSE(AI&ML) of ACE Engineering College.
World Journal of Advanced Research and Reviews, 2025, 25(01), 850-853
Article DOI: 10.30574/wjarr.2025.25.1.0123
Received on 03 December 2024; revised on 08 January 2025; accepted on 10 January 2025
Fashion image generation is a significant challenge at the intersection of artificial intelligence (AI) and creative industries, with applications in design, e-commerce, and virtual try-on systems. Conditional Generative Adversarial Networks (CGANs) extend the capabilities of standard GANs by allowing control over generated content based on specified conditions, such as clothing type, color, or texture. This Study investigates the use of CGANs for generating high-quality, attribute-specific fashion images. The study includes designing a CGAN architecture, training the model on the Deep Fashion dataset, and optimizing performance through rigorous experimentation
Fashion image generation; Generative Adversarial; Conditional Generative Adversarial Networks (CGANs); CGAN Architecture
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P. Kamakshi Thai, Sai Jayanth Bandaru, Abhishek Sharma and Akshay Devala. Fashion image generation using generative adversarial neural network. World Journal of Advanced Research and Reviews, 2025, 25(01), 850-853. Article DOI: https://doi.org/10.30574/wjarr.2025.25.1.0123.
Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0