Deep Learning Approaches for Text-to-Image Generation: Progress and Performance Analysis

Abstract

Synthesizing images from natural language descriptions is the subject of the quickly developing artificial intelligence field known as "text-to-image generation." This study examines the latest developments in deep learning techniques, with a focus on Generative Adversarial Networks (GANs) and diffusion models like Imagen, GLIDE, and DALL•E. The model architectures, training datasets, and evaluation measures are all thoroughly compared. The paper also emphasizes the limits of existing systems and real-world applications. We wrap up with recommendations for future study, highlighting the necessity of increased fidelity, controllability, and ethical considerations when deploying models.