TY - JOUR
T1 - Loss functions of generative adversarial networks (gans)
T2 - Opportunities and challenges
AU - Pan, Zhaoqing
AU - Yu, Weijie
AU - Wang, Bosi
AU - Xie, Haoran
AU - Sheng, Victor S.
AU - Lei, Jianjun
AU - Kwong, Sam
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2020/8
Y1 - 2020/8
N2 - 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.
AB - 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.
KW - Loss functions
KW - computational intelligence
KW - deep learning
KW - generative adversarial networks (GANs)
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85085747332&partnerID=8YFLogxK
U2 - 10.1109/TETCI.2020.2991774
DO - 10.1109/TETCI.2020.2991774
M3 - Article
AN - SCOPUS:85085747332
SN - 2471-285X
VL - 4
SP - 500
EP - 522
JO - IEEE Transactions on Emerging Topics in Computational Intelligence
JF - IEEE Transactions on Emerging Topics in Computational Intelligence
IS - 4
M1 - 9098081
ER -