首页|Advancements in adversarial generative text-to-image models: a review
Advancements in adversarial generative text-to-image models: a review
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NETL
NSTL
Taylor & Francis
This comprehensive study explores the landscape of Text-to-image Generative Adversarial Networks (T2I-GANs), aiming to spot the light on their architecture, evaluation methodologies, and limitations. It begins with an overview of the GAN paradigm and then classifies and analyzes various models based on their architecture. The study examines the most common evaluation metrics and datasets used in the field, providing detailed comparisons that offer insight into models' architectures and performances. Additionally, it discusses the diverse experiments performed for model assessment and the limitations reported in existing research, highlighting challenges and potential areas for improvement. This exploration aims to serve as a valuable resource for researchers, practitioners, and enthusiasts interested in the evolving domain of T2l-GANs.
NETL
NSTL
Taylor & Francis
