Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies

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 translation. We organize the literature based on their base models, developed ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets. We present milestones of adversarial models, review an extensive selection of previous works in various categories, and present insights on the development route from the model-based to data-driven methods. Further, we highlight a range of potential future research directions. One of the unique features of this review is that all software implementations of these GAN methods and datasets have been collected and made available in one place at https://github.com/pshams55/GAN-Case-Study.