Abstract
Generative AI has revolutionized the field of digital art by enabling automated image creation that mimics human artistry. Traditional approaches often need more thematic coherence and help to capture complex styles in artistic domains. In order to overcome these constraints, we present a Generative Adversarial Network (GAN) model integrated with a Semantic Web architecture, therefore providing automated painting creation for creative businesses. Our strategy guarantees thematic and stylistic congruence by using Semantic Web methods to include contextual information and GANs to create high-quality paintings from noise. Our GAN produces reasonable and varied creative outputs. Experimental findings show how flexible the model is for different artistic topics, therefore helping applications in the creative sectors via improved AI-driven artistic production.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call