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

**Authors:** Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Huiyu Zhou, Ruili Wang, M. Emre Celebi, Jie Yang  
**arXiv:** [2012.13736](https://arxiv.org/abs/2012.13736)  
**Date:** 2020-12-26  
**Summary page:** https://www.summarizepaper.com/en/paper/2012.13736v1/

## Key points

- Generative Adversarial Networks (GANs) are powerful models for learning complex distributions and synthesizing semantically meaningful samples in various domains.
- GANs have the ability to transform objects or persons into desired shapes, making them a focal point of research.
- The paper "Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies" by Pourya Shamsolmoali et al. provides an extensive review of adversarial models for image synthesis.
- The survey covers synthetic image generation methods across categories like image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image translation.
- The authors organize the literature based on base models and explore ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets.
- The review includes milestones in adversarial models' evolution and reviews previous works in various categories.
- A unique aspect is the inclusion of all software implementations of GAN methods and datasets in one accessible location at https://github.com/pshams55/GAN-Case-Study.
- This resource serves as a valuable repository for researchers exploring different GAN techniques for image synthesis.
- The comprehensive survey consolidates existing knowledge and points towards potential future research directions in generative adversarial networks for image synthesis.

## Summary

Generative Adversarial Networks (GANs) have emerged as powerful models for learning complex distributions and synthesizing semantically meaningful samples in various application domains such as computer vision, medicine, and natural language processing. The ability of GANs to transform objects or persons into desired shapes has been a focal point of research in this field. Despite the rapid development of GANs, there is a lack of comprehensive reviews that encompass all aspects of adversarial models for image synthesis. In their paper titled "Image Synthesis with Adversarial Networks: a Comprehensive Survey and Case Studies," authors Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Huiyu Zhou, Ruili Wang, M. Emre Celebi, and Jie Yang address this gap by providing an extensive review of adversarial models for image synthesis. The survey covers synthetic image generation methods across different categories including image-to-image translation, fusion image generation, label-to-image mapping, and text-to-image translation. The authors organize the literature based on base models and delve into developed ideas related to architectures, constraints, loss functions, evaluation metrics, and training datasets. They highlight milestones in adversarial models' evolution and review a wide selection of previous works in various categories. Additionally,<fd> insights are provided on the development route from model-based to data-driven methods.</fd> A unique aspect of this review is the inclusion of all software implementations of GAN methods and datasets in one accessible location at https://github.com/pshams55/GAN-Case-Study. This resource serves as a valuable repository for researchers looking to explore different GAN techniques for image synthesis. Overall,<fd> this comprehensive survey not only consolidates existing knowledge but also points towards potential future research directions in the field of generative adversarial networks for image synthesis.</fd> By offering a detailed analysis of diverse approaches and methodologies used in GANs development, this study contributes significantly to advancing the understanding and application of these models in practical scenarios.