Generative Artificial Intelligence (AI) in computer vision
Abstract
Generative AI has revolutionized computer vision by enabling machines to synthesize and enhance visual data. Advances in deep learning, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and diffusion models, have led to high-quality image generation with applications in medical imaging, data augmentation, and autonomous systems. Despite its potential, challenges like ethical concerns, dataset biases, and computational costs remain critical for future research and implementation. This paper explores the evolution of generative AI, its methodologies, applications, and the ethical and computational challenges it presents.
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