Fashion image generation using generative adversarial neural network

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