留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于深度卷积的残差三生网络研究与应用

厉铮泽 杨小远 朱日东 王敬凯

厉铮泽, 杨小远, 朱日东, 等 . 基于深度卷积的残差三生网络研究与应用[J]. 北京航空航天大学学报, 2019, 45(9): 1864-1873. doi: 10.13700/j.bh.1001-5965.2018.0766
引用本文: 厉铮泽, 杨小远, 朱日东, 等 . 基于深度卷积的残差三生网络研究与应用[J]. 北京航空航天大学学报, 2019, 45(9): 1864-1873. doi: 10.13700/j.bh.1001-5965.2018.0766
LI Zhengze, YANG Xiaoyuan, ZHU Ridong, et al. Research and application of residual triplet network based on deep convolution[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1864-1873. doi: 10.13700/j.bh.1001-5965.2018.0766(in Chinese)
Citation: LI Zhengze, YANG Xiaoyuan, ZHU Ridong, et al. Research and application of residual triplet network based on deep convolution[J]. Journal of Beijing University of Aeronautics and Astronautics, 2019, 45(9): 1864-1873. doi: 10.13700/j.bh.1001-5965.2018.0766(in Chinese)

基于深度卷积的残差三生网络研究与应用

doi: 10.13700/j.bh.1001-5965.2018.0766
基金项目: 

国家自然科学基金 61671002

详细信息
    作者简介:

    厉铮泽 男, 硕士研究生。主要研究方向:深度学习与应用

    杨小远 女, 博士, 教授, 博士生导师。主要研究方向:深度学习与图像处理

    朱日东 男, 博士研究生。主要研究方向:深度学习与目标跟踪

    王敬凯 男, 博士研究生。主要研究方向:深度学习与图像融合

    通讯作者:

    杨小远, E-mail: xiaoyuanyang@vip.163.com

  • 中图分类号: O29

Research and application of residual triplet network based on deep convolution

Funds: 

National Natural Science Foundation of China 61671002

More Information
  • 摘要:

    针对图像多分类任务,提出基于深度卷积的残差三生网络,旨在通过残差学习和距离比较来训练神经网络得到有效的特征表示。首先,设计了一个21层的深度卷积神经网络作为三生网络的嵌入网络,其中该卷积网络共连接6个块(block)。利用残差学习的方式,每个block的输出层由卷积层的输出和该block的输入共同组成,降低网络学习难度,避免网络出现退化问题。然后,每个block中采用相同拓扑结构分路的卷积层,拓宽网络的宽度。最后,在全连接层拼接了来自前面卷积层和block的输出,加强特征信息的传递。训练前,针对正负样本采用交叉组合的采样方法来增加有效训练样本量;训练期间,用样本中心点更换原点样本作为输入,能平均降低0.5%错误率。在与其他三生网络的对比实验中,在MNIST、CIFAR10和SVHN数据库上达到最好的效果,在所有分类网络中,本文网络在MNIST上达到最好的效果,在CIFAR10和SVHN上表现优异。

     

  • 图 1  三生网络

    Figure 1.  Triplet network

    图 2  嵌入网络结构

    Figure 2.  Embedded network structure

    图 3  ResNeXt[22]的等价网络结构

    Figure 3.  Equivalent network structure of ResNeXt[22]

    图 4  三元组选择

    Figure 4.  Triplet selection

    图 5  二维欧氏空间样本中心点示例

    Figure 5.  2D European space sample center point example

    图 6  本文网络与DenseNet-BC和ResNeXt-29的CIFAR10测试曲线

    Figure 6.  CIFAR10 testing curves of proposed network, DenseNet-BC and ResNeXt-29

    图 7  CIFAR10-二维特征表示

    Figure 7.  CIFAR10-2D feature representation

    图 8  MNIST-二维特征表示

    Figure 8.  MNIST-2D feature representation

    图 9  SVHN-二维特征表示

    Figure 9.  SVHN-2D feature representation

    表  1  网络配置

    Table  1.   Network configuration

    网络层 通道数变化 步长/填充量 池化层
    stage1 block1 conv3 3-64 s=1, p=1 avgpool
    conv1 64-512 s=1, p=0
    conv3 512-512 s=2, p=1
    conv1 512-256 s=1, p=0
    block2 conv1 256-512 s=1,p=0
    conv3 512-512 s=1, p=1
    conv1 512-256 s=1, p=0
    block3 conv1 256-512 s=1, p=0
    conv3 512-512 s=1, p=1
    conv1 512-256 s=1, p=0 avgpool
    stage2 block1 conv1 256-1 024 s=1, p=0
    conv3 1 024-1 024 s=2,p=1
    conv1 1 024-512 s=1, p=0
    block2 conv1 512-1 024 s=1, p=0
    conv3 1 024-1 024 s=1, p=1
    conv1 1 024-512 s=1, p=0
    block3 conv1 512-1 024 s=1, p=0
    conv3 1 024-1 024 s=1, p=1
    conv1 1 024-512 s=1, p=0 avgpool
    fc-32
    注:以32×32RGB图像为例。
    下载: 导出CSV

    表  2  CIFAR10上的错误率

    Table  2.   Error rates on CIFAR10

    网络 错误率/%
    Stochastic Pooling[25] 15.13
    TripletNet[31] 12.90
    Maxout Network[26] 11.68
    AlexNet[3] 11
    DSN[28] 9.69
    NIN[27] 8.8
    MLDNN[30] 8.12
    CMC[29] 6.87
    ResNet[6] 6.43
    DenseNet[20] 5.83
    DenseNet-BC[20] 5.19
    本文网络 4.28
    ResNeXt-29[22] 3.58
    下载: 导出CSV

    表  3  网络训练迭代次数(CIFAR10)

    Table  3.   Iteration times of network training (CIFAR10)

    网络 迭代次数 错误率/%
    ResNet[6] 60 000 6.43
    Maxout Network[26] 700 11.68
    Stochastic Pooling[25] 500 15.13
    本文网络 310 4.28
    MLDNN[30] 300 8.12
    DenseNet-BC[20] 300 5.19
    DenseNet[20] 300 5.83
    ResNeXt-29[22] 300 3.58
    NIN[27] 200 8.8
    DSN[28] 110 9.69
    下载: 导出CSV

    表  4  MNIST上的错误率

    Table  4.   Error rates on MNIST

    网络 错误率/%
    Stochastic Pooling[25] 0.47
    Maxout Network[26] 0.47
    NIN[27] 0.47
    MLDNN[30] 0.42
    DSN[28] 0.39
    TripletNet[31] 0.38
    CMC[29] 0.33
    本文网络 0.22
    下载: 导出CSV

    表  5  SVHN上的错误率

    Table  5.   Error rates on SVHN

    网络 错误率/%
    TripletNet[31] 4.63
    Stochastic Pooling[25] 2.80
    Maxout Network[26] 2.67
    NIN[27] 2.35
    DSN[28] 1.92
    MLDNN[30] 1.92
    本文网络 1.81
    CMC[29] 1.76
    DenseNet-BC[20] 1.74
    DenseNet[20] 1.59
    下载: 导出CSV

    表  6  样本组合的实验对比(CIFAR10)

    Table  6.   Experimental comparison of sample combination (CIFAR10)

    正负样本组合 训练时间/s 错误率/%
    1正1负 230 4.74
    1正2负 410 4.52
    2正2负 750 4.28
    2正3负 1190 4.29
    下载: 导出CSV
  • [1] LECUN Y, BOTTOU L, BENGIO Y, et al.Gradient-based learning applied to document recognition[J].Proceedings of the IEEE, 1998, 86(11):2278-2324. doi: 10.1109/5.726791
    [2] RUSSAKOVSKY O, DENG J, SU H, et al.ImageNet large scale visual recognition challenge[J].International Journal of Computer Vision, 2014, 115(3):211-252. http://www.wanfangdata.com.cn/details/detail.do?_type=perio&id=3f4feabdfaad8008975391cb35d2e74c
    [3] KRIZHEVSKY A, SUTSKEVER I, HINTON G.Imagenet classification with deep convolutional neural networks[C]//International Conference on Neural Information Processing Systems.New York: Curran Associates Inc., 2012: 1097-1105.
    [4] SIMONYAN K, ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[C]//International Conference on Learning Representations, 2015: 1-14.
    [5] SZEGEDY C, LIU W, JIA Y.Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2015: 1-9.
    [6] HE K M, ZHANG X Y, REN S Q, et al.Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2016: 770-778.
    [7] SZEGEDY C, VINCENT V, IOFFE S.Rethinking the inception architecture for computer vision[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2016: 2818-2826.
    [8] GIRSHICK R, DONAHUE J, DARRELL T, et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2014: 580-587.
    [9] WANG N, YEUNG D Y.Learning a deep compact image representation for visual tracking[C]//International Conference on Neural Information Processing Systems.New York: Curran Associates Inc., 2013: 809-817.
    [10] KARPATHY A, TODERICI G, SHETTY S, et al.Large-scale video classification with convolutional neural networks[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2014: 1725-1732.
    [11] KANG B N, KIM K Y, KIM D J.Deep convolutional neural network using triplets of faces, deep ensemble, and score-level fusion for face recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2017: 611-618.
    [12] WANG C, LAN X P, ZHANG X.How to train triplet networks with 100K identities [C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2017: 1907-1915.
    [13] SCHROFF F, KALENICHENKO D, PHILBIN J.Facenet: A unified embedding for face recognition and clustering[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2015: 815-823.
    [14] LIU Y S, HUANG C.Scene classification via triplet networks[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2018, 11(1):220-237. doi: 10.1109/JSTARS.2017.2761800
    [15] HERMANS A, BEYER L, LEIBE B.In defense of the triplet loss for person re-identification[EB/OL].(2017-11-21)[2018-12-01].https://arxiv.org/paf/1703.07737.pdf.
    [16] LIU H, TIAN Y, WANG Y, et al.Deep relative distance learning tell the difference between similar vehicles[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2016: 2167-2175.
    [17] CHENG D, GONG Y H, ZHOU S P, et al.Person re-identification by multi-channel parts-based CNN with improved triplet loss function[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2016: 1335-1344.
    [18] ZHANG S, GONG Y, WANG J.Deep metric learning with improved triplet loss for face clustering in video[C]//Pacific-rim Conference on Advances in Multimedia Information Processing.Berlin: Springer, 2016: 497-508.
    [19] CHEN W, CHEN X, ZHANG J, et al.Beyond triplet loss: A deep quadruplet network for person re-identification[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2017: 1320-1329.
    [20] HUANG G, LIU Z, MAATEN L, et al.Densely connected convolutional networks[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2017: 2261-2269.
    [21] SZEGEDY C, IOFFE S, VANHOUCKE V, et al.Inception-v4, inception-resnet and the impact of residual connections on learning[C]//AAAI Conference on Artifical Intelligence.Palo Atlo, CA: AAAI Press, 2017: 4278-4284.
    [22] XIE S, GIRSHICK R, DOLLAR P, et al.Aggregated residual transformations for deep neural networks[C]//IEEE Conference on Computer Vision and Pattern Recognition.Piscataway, NJ: IEEE Press, 2017: 5987-5995.
    [23] IOFFE S, SZEGEDY C.Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]//International Conference on Machine Learning.Boston: MIT Press, 2015: 448-456.
    [24] DING S, LIN L, WANG G, et al.Deep feature learning with relative distance comparison for person re-identification[J].Pattern Recognition, 2015, 48(10):2993-3003. doi: 10.1016/j.patcog.2015.04.005
    [25] ZEILER M D, FERGUS R.Stochastic pooling for regularization of deep convolutional neural networks[EB/OL].(2013-01-16)[2018-11-25].https://arxiv.org/pdf/1301.3557.pdf.
    [26] GOODFELLOW I J, WARDE-FARLEY D, MIRZA M, et al.Maxout networks[C]//Proceedings of the International Conference on Machine Learning.Boston: MIT Press, 2013: 1319-1327.
    [27] LIN M, CHEN Q, YAN S.Network in network[C]//International Conference on Learning Representations, 2014: 1-10.
    [28] LEE C Y, XIE S N, GALLAGHER P W, et al.Deeply-supervised nets[C]//Proceedings of the International Conference on Artificial Intelligence and Statistics.San Diego, California: PMLR, 2015: 562-570.
    [29] LIAO Z B, CARNEIRO G.Competitive multi-scale convolution[EB/OL].(2015-11-18)[2018-11-10].https://arxiv.org/pdf/1511.05635.pdf.
    [30] XU C Y, LU C Y, LIANG X D, et al.Multi-loss regularized deep neural network[J].IEEE Transactions on Circuits and Systems for Video Technology, 2016, 26(12):2273-2283. doi: 10.1109/TCSVT.2015.2477937
    [31] HOFFER E, AILON N.Deep metric learning using triplet network[C]//International Workshop on Similarity-based Pattern Recognition.Berlin: Springer, 2015: 84-92.
    [32] KRIZHEVSKY A, HINTON G.Learning multiple layers of features from tiny images[D].Toronto: University of Toronto, 2009: 32-35.
    [33] NETZER Y, WANG T, COATES A, et al.Reading digits in natural images with unsupervised feature learning[C]//NIPS Workshop on Deep Learning and Unsupervised Feature Learning.New York: Curran Associates Inc., 2011: 1-9.
  • 加载中
图(9) / 表(6)
计量
  • 文章访问数:  323
  • HTML全文浏览量:  2
  • PDF下载量:  306
  • 被引次数: 0
出版历程
  • 收稿日期:  2018-12-26
  • 录用日期:  2019-02-02
  • 刊出日期:  2019-09-20

目录

    /

    返回文章
    返回
    常见问答