基于改进Fisher准则的深度卷积生成对抗网络算法  

Deep convolutional generative adversarial network algorithm based on improved fisher’s criterion

作　　者：张浩 齐光磊 侯小刚 郑凯梅 ZHANG Hao;QI Guanglei;HOU Xiaogang;ZHENG Kaimei(Century College,Beijing University of Posts and Telecommunications,Beijing 102101,China;College of Information Science and Engineering,Xinjiang University,Urumqi 830046,China)

机构地区：[1]北京邮电大学世纪学院计算机科学与技术系,北京102101 [2]新疆大学信息科学与工程学院,新疆乌鲁木齐830046

出　　处：《光学精密工程》2022年第24期3239-3249,共11页Optics and Precision Engineering

基　　金：国家自然科学基金资助项目(No.61806014)。

摘　　要：针对当训练样本量不足或者迭代次数降低时生成图像质量急剧下降的问题,提出了一种基于改进Fisher准则的深度卷积生成对抗网络算法(FDCGAN,Deep Convolutional Generative Adversarial Network algorithm based on improved Fisher’s criterion)。该方法在判别模型中添加线性层,用来提取类别信息。在反向传播中采用基于Fisher的约束准则,结合标签和类别信息,在权值的迭代调整时既考虑误差的最小化,又同时让样本保持类内距离小、类间距离大,从而使权值能更加快速地逼近最优值。通过与最新不同的6个网络模型进行对比实验,FDCGAN模型在FID指标上均取得了较好的效果。此外,通过将该方法运用到目前先进模型上进行泛化测试,实验结果均取得较理想的效果。An improved Fisher’s criterion-based deep convolutional generative adversarial network algorithm(FDCGAN)is proposed in this study to solve the problem of quality deterioration in generated images when the training sample size is insufficient or number of iterations decreases. In this method,a linear layer is added to the discriminative model to extract category information. Then,Fisher’s criterion is used in backpropagation to combine label and category information. To minimize errors,the weights are adjusted iteratively while maintaining small intra-class and large inter-class distances such that the weights can rapidly approach the optimal value. A comparison of the experimental results of the FDCGAN model with that of the most recent six network models shows that the proposed model achieves better performance in all the FID metrics. In addition,applying the proposed model to the current advanced models in generalization tests yields more satisfactory results.

关 键 词：深度卷积生成对抗网络 FISHER准则 反向传播算法 FID评价指标 

分 类 号：TP394.1[自动化与计算机技术—计算机应用技术]