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

2020IEEE Transactions on Emerging Topics in Computational Intelligence101 citationsDOI

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.

Topics & Concepts

Adversarial systemComputer scienceGenerative grammarTask (project management)Key (lock)Generative adversarial networkMeasure (data warehouse)Artificial intelligenceFunction (biology)Machine learningData miningDeep learningComputer securityManagementEconomicsBiologyEvolutionary biologyGenerative Adversarial Networks and Image SynthesisAdvanced Image Processing TechniquesDigital Media Forensic Detection