计算机科学 ›› 2019, Vol. 46 ›› Issue (9): 243-249.doi: 10.11896/j.issn.1002-137X.2019.09.036
王格格, 郭涛, 李贵洋
WANG Ge-ge, GUO Tao, LI Gui-yang
摘要: 生成对抗网络(GAN)是目前图像生成领域中一种新的、有效的训练生成模型方法。深度卷积生成对抗网络(DCGAN)作为GAN的一种延伸,将卷积神经网络引入到生成模型中进行无监督训练。但DCGAN的线性卷积层对于下层数据块是一个广义线性模型,其抽象层次较低,生成的图像质量不高,并且在模型性能度量方面仅以主观的视觉感受来评判图像质量。针对以上问题,文中提出了一种多层感知器深度卷积生成对抗网络(MPDCGAN),采用多层感知器卷积层取代广义线性模型在输入数据上进行卷积,以捕获图像更深层次的特征,并采用定量评估方法Frechet Inception Distance(FID)衡量图像生成质量。在4种基准数据集上的实验结果表明,采用MPDCGAN生成的图像的FID值与图像质量呈负相关关系,且图像生成质量随着FID值的降低得到了进一步的提高。
中图分类号:
[1]LECUN Y,BENDIO Y,HINTON G.Deep Learning[J].Nature,2015,521(7553):436-444. [2]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative Adversarial Nets[C]//International Conference on Neural Information Processing Systems.MIT Press,2014:2672-2680. [3]WANG W L,LI Z R.Advances in Generative Adversarial Network[J].Journal on Communications,2018,39(2):135-148.(in Chinese)王万良,李卓蓉.生成式对抗网络研究进展[J].通信学报,2018,39(2):135-148. [4]LIN Y L,DAI X Y,LI L,et al.The New Frontier of AI Re-search:Generative Adversarial Networks[J].ACTA Automatica Sinica,2018,44(5):775-792.(in Chinese)林懿伦,戴星原,李力,等.人工智能研究的新前线:生成式对抗网络[J].自动化学报,2018,44(5):775-792. [5]WANG K F,GOU C,DUAN Y J,et al.Generative Adversarial Networks:the state of the art and beyond[J].ACTA Automatica Sinica,2017,43(3):321-332.(in Chinese)王坤峰,苟超,段艳杰,等.生成式对抗网络GAN的研究进展与展望[J].自动化学报,2017,43(3):321-332. [6]RATLIFF L J,BURDEN S A,SASTRY S S.Characterizationand Computation of Local Nash Equilibria in Continuous Games[C]//2013 51st Annual Allerton Conference on Communication,Control,and Computing (Allerton).IEEE,2013:917-924. [7]LEI Y,DING X,WANG S.Visual Tracker using SequentialBayesian Learning:Discriminative,Generative,and Hybrid[J].IEEE Transactions on Systems Man & Cybernetics Part B,2008,38(6):1578-1591. [8]DINH T B,MEDIONI G.Co-training Framework of Generative and Discriminative Trackers with Partial Occlusion Handling[C]//2011 IEEE Workshop on Applications of Computer Vision (WACV).IEEE,2011:642-649. [9]ARJOVSKY M,CHINTALA S,BOTTOU L.Wasserstein GAN[J].arXiv:1701.07875,2017. [10]GULRAJANI I,AHMED F,ARJOVSKY M,et al.ImprovedTraining of Wasserstein Gans [M]//Advances in Neural Information Processing Systems.Berlin:Springer,2017:5767-5777. [11]MIRZA M,OSINDERO S.Conditional Generative AdversarialNets[J].arXiv:1411.1784,2014. [12]智能算法研究学习1688.GAN与cGAN [EB/OL].http://www.jinciwei.cn/h303087.html. [13]CHEN X,DUAN Y,HOUTHOOFT R,et al.Infogan:Inter-pretable Representation Learning by Information Maximizing Generative A-dversarial Nets[M]//Advances in Neural Information Processing Systems.Berlin:Springer,2016:2172-2180. [14]PERARNAU G.一文帮你发现各种出色的GAN变体[EB/OL].https://chuansongme.com/n/1711678142321. [15]RADFORD A,METZ L,CHINTALA S.Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks[J].arXiv:1511.06434,2015. [16]LIN M,CHEN Q,YAN S.Network In Network[J].arXiv:1312.4400,2013. [17]HEUSEL M,RAMSAUER H,UNTERTHINER T,et al.GANs Trained by A Two Time-scale Update Rule Converge to A Nash equilibrium[J].arXiv:1706.08500,2017. [18]KRIZHEVAKY A,SUTSKEVER I,HINTON G E.ImageNet Classification with Deep Convolutional Neural Networks[C]//International Conference on Neural Information Processing Systems.Curran Associates Inc.,2012:1097-1105. [19]BENGIO Y,COURVILLE A,VINCENT P.RepresentationLearning:A Review and New Perspectives[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2013,35(8):1798-1828. [20]知乎.CNN网络的Pooling层有什么用.https://www.zhihu.com/question/36686900. [21]DENG J,DONG W,SOCHER R,et al.ImageNet:A Large-scale Hierarchical Image Database[C]//2009 IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2009. [22]THEIS L,OORD A,BETHGE M.A Note on the Evaluation of Generative Models[J].arXiv:1511.01844,2015. [23]SALIMANS T,GOODFELLOW I,ZAREMBA W,et al.Im-proved Techniques for Training Gans[M]//Advances in Neural Information Processing Systems.Berlin:Springs,2016:2234-2242. [24]BARRATT S,SHARMA R.A Note on the Inception Score[J].arXiv:1801.01973,2018. [25]百度百科.损失函数[EB/OL].https://baike.baidu.com/item/%E6%8D%9F%E5%A4%B1%E5%87%BD%E6%95%B0/1783236?fr=aladdin. [26]YUAN M.卷积神经网络的复杂度分析[EB/OL].https://zhuanlan.zhihu.com/p/31575074. |
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