The research of virtual face based on Deep Convolutional Generative Adversarial Networks using TensorFlow

Shouqiang Liu, Mengjing Yu, Miao Li and Qingzhen Xu

Physica A: Statistical Mechanics and its Applications, 2019, vol. 521, issue C, 667-680

Abstract: Since Generative Adversarial Nets (GANs) has been proposed in 2014, it has become one of the most popular hot topics. Deep Convolutional Generative Adversarial Networks (DCGAN) is greatly promoted the development and application of GANs. In this paper, we have made an in-depth exploration for the most popular DCGAN at present via utilizing TensorFlow deep learning framework, using the open CelebA face dataset of The Chinese University of Hong Kong as the data source. By comparing DCGAN unconstrained and DCGAN constrained, the experimental results show that the DCGAN model significantly improves the virtual face generation model after adding constraints in the training phase, which enhance the ability of the generator to deceive the discriminator. Finally, we have evaluated the proposed model from the perspective of TensorBoard and achieved the desired experimental results.

Keywords: Deep Convolutional Generative Adversarial Networks (DCGAN); TensorFlow; CelebA; Virtual face (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:521:y:2019:i:c:p:667-680

DOI: 10.1016/j.physa.2019.01.036

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