基于半监督学习生成对抗网络的人脸还原算法研究

基于大量训练样本生成高置信度图像的生成对抗网络研究已经取得一些成果，但是现有的研究只针对已知训练样本进行图像生成，而未将训练的参数用于训练样本之外的图像生成。该文设计了一种改进的生成对抗网络模型，在已有网络的基础上增加一个还原层，使得测试图像可以通过改进的对抗网络生成对应的高置信度图像。实验结果表明，改进的生成对抗网络参数可以应用到训练集之外的普通样本。同时本文改进了生成模型的损失算法，极大地缩短了网络的收敛时间。

Research on Face Reduction Algorithm Based on Generative Adversarial Nets with Semi-supervised Learning

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.