Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning
CAO Zhiyi NIU Shaozhang ZHANG Jiwei
(Beijing Key Laboratory of Intelligent Telecommunication Software and Multimedia, Beijing University of Posts and Telecommunications, Beijing 100876, China)
Abstract：Based on a large number of training samples to generate high confidence images, generative adversarial nets achieve good results, but the existing network of image generation in the training sample basis, the training parameters can not be used to generate images outside of training samples. In this paper, an improved generative adversarial nets model is proposed, and a reduction layer is added on the basis of the existing network, so that the test image can generate the corresponding high confidence image through the improved generative adversarial nets. The experimental results show that the improved generative adversarial nets parameters can be applied to the common samples outside the training set. At the same time, this paper improves the loss algorithm of the generated model, which greatly shortens the convergence time of the network.
曹志义, 牛少彰,张继威. 基于半监督学习生成对抗网络的人脸还原算法研究[J]. 电子与信息学报, 2018, 40(2): 323-330.
CAO Zhiyi, NIU Shaozhang, ZHANG Jiwei. Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning. JEIT, 2018, 40(2): 323-330.
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