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
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 - computational intelligence
KW - Computational modeling
KW - deep learning
KW - Gallium nitride
KW - Generative adversarial networks
KW - generative adversarial networks (GANs)
KW - Generators
KW - Linear programming
KW - Loss functions
KW - machine learning
KW - Task analysis
KW - Training
UR - http://www.scopus.com/inward/record.url?scp=85085747332&partnerID=8YFLogxK
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-85085747332&origin=recordpage
U2 - 10.1109/TETCI.2020.2991774
DO - 10.1109/TETCI.2020.2991774
M3 - RGC 21 - Publication in refereed journal
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
ER -