Loss Functions of Generative Adversarial Networks (GANs): Opportunities and Challenges

Zhaoqing PAN, Weijie YU, Bosi WANG, Haoran XIE, Victor S. SHENG, Jianjun LEI*, Sam KWONG

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)

Abstract

Recently, the Generative Adversarial Networks (GANs) are fast becoming a key promising research direction in computational intelligence. To improve the modeling ability of GANs, loss functions are used to measure the differences between samples generated by the model and real samples, and make the model learn towards the goal. In this paper, we perform a survey for the loss functions used in GANs, and analyze the pros and cons of these loss functions. Firstly, the basic theory of GANs, and its training mechanism are introduced. Then, the loss functions used in GANs are summarized, including not only the objective functions of GANs, but also the application-oriented GANs’ loss functions. Thirdly, the experiments and analyses of representative loss functions are discussed. Finally, several suggestions on how to choose appropriate loss functions in a specific task are given.
Original languageEnglish
Number of pages23
JournalIEEE Transactions on Emerging Topics in Computational Intelligence
Volume4
Issue number4
Early online date21 May 2020
DOIs
Publication statusPublished - Aug 2020

Bibliographical note

This work was supported in part by the National Natural Science Foundation of China under Grant 61971232, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-041, in part by the Natural Science Foundation of Tianjin Under Grant 18ZXZNGX00110 and 18JCJQJC45800. Paper no. TETCI-2019-0268. (Weijie Yu and Bosi Wang contribute equally to this work) (Corresponding author: Jianjun Lei.)

Keywords

  • Computational modeling
  • Gallium nitride
  • Generative adversarial networks
  • Generators
  • Linear programming
  • Loss functions
  • Task analysis
  • Training
  • computational intelligence
  • deep learning
  • generative adversarial networks (GANs)
  • machine learning

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