Image synthesis with adversarial networks: A comprehensive survey and case studies

Pourya Shamsolmoali, Masoumeh Zareapoor, Eric Granger, Huiyu Zhou, Ruili Wang, M. Emre Celebi, Jie Yang*

*Corresponding author for this work

Research output: Contribution to journalShort surveypeer-review

1 Citation (Scopus)

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.

Original languageEnglish
Pages (from-to)126-146
Number of pages21
JournalInformation Fusion
Volume72
Early online date27 Feb 2021
DOIs
Publication statusEarly online date - 27 Feb 2021
Externally publishedYes

Bibliographical note

Funding Information:
This publication is partly supported by the NSFC, China (No. 61876107 , U1803261 ), Committee of Science and Technology, Shanghai, China (No. 19510711200 ), and the National Science Foundation under Grant No. 1946391 .

Publisher Copyright:
© 2021 Elsevier B.V.

Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.

Keywords

  • Classification
  • GANs
  • Image fusion
  • Image synthesis
  • Image-to-image translation

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Information Systems
  • Hardware and Architecture

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