Comparative Analysis of Generative AI Models: GANs vs. Diffusion Models
Abstract
Generative AI models have gained significant attention for their ability to create high-quality synthetic data. This paper presents a comparative study between Generative Adversarial Networks (GANs) and diffusion models, focusing on image synthesis, text generation, and data augmentation. We evaluate their performance based on sample quality, training stability, and computational cost. The findings provide insights into the strengths and weaknesses of each model, guiding their application in fields such as healthcare, entertainment, and creative AI.
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