A research on generative adversarial network algorithm based on GPU parallel acceleration

Haibo Chen; Tao Jia; Jing Tang

doi:10.1117/12.2539238

27 November 2019 A research on generative adversarial network algorithm based on GPU parallel acceleration

Haibo Chen, Tao Jia, Jing Tang

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113211R (2019) https://doi.org/10.1117/12.2539238
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

Abstract

In recent years, Generative Adversarial Network (GAN) has received much attention in the field of machine learning. It is an unsupervised learning model which is widely used in image, video, voice, etc. Based on GAN's two-man zero-sum game theory, the researchers proposed excellent variant algorithms such as deep convolutional GAN(DCGAN), Conditional GAN(CGAN), Least Squares GAN(LSGAN), and Boundary Equilibrium GAN (BEGAN), which has gradually overcome the problem of training imbalance and model collapse. However, the time efficiency of model training has always been a challenging problem. This paper proposes a GAN algorithm based on GPU parallel acceleration, which utilizes the powerful computing power of GPU and the advantages of multi-parallel computing, greatly reduces the time of model training, improves the training efficiency of GAN model, and achieves better modeling performance. Finally, we used the LSUN public scene dataset and the TIMIT public voice dataset to evaluate the proposed algorithm and compare it with the traditional GAN, DCGAN, LSGAN, and BEGAN algorithms. The experiment has fully proved the time advantage of the model training of the algorithm introduced in this paper.

Citation Download Citation

Haibo Chen, Tao Jia, and Jing Tang "A research on generative adversarial network algorithm based on GPU parallel acceleration", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113211R (27 November 2019); https://doi.org/10.1117/12.2539238

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available