TL;DR
This comprehensive survey reviews the development, categories, and techniques of Generative Adversarial Networks (GANs) for image synthesis, including loss functions, evaluation metrics, and training stability, with case studies and software resources.
Contribution
It provides an organized, detailed review of GAN architectures, loss variants, and evaluation methods, along with a collection of software implementations and datasets.
Findings
Summarizes key milestones in GAN development.
Categorizes GAN-based image synthesis methods.
Provides insights into future research directions.
Abstract
Generative Adversarial Networks (GANs) have been extremely successful in various application domains such as computer vision, medicine, and natural language processing. Moreover, transforming an object or person to a desired shape become a well-studied research in the GANs. GANs are powerful models for learning complex distributions to synthesize semantically meaningful samples. However, there is a lack of comprehensive review in this field, especially lack of a collection of GANs loss-variant, evaluation metrics, remedies for diverse image generation, and stable training. Given the current fast GANs development, in this survey, we provide a comprehensive review of adversarial models for image synthesis. We summarize the synthetic image generation methods, and discuss the categories including image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image…
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