Computer Science ›› 2021, Vol. 48 ›› Issue (6): 131-137.doi: 10.11896/jsjkx.210100008

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Image Recognition with Deep Dynamic Joint Adaptation Networks

LIU Yu-tong1, LI Peng1,2,3, SUN Yun-yun4, HU Su-jun1   

  1. 1 School of Computer Science,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    2 Nanjing Center of HPC China,Nanjing 210023,China
    3 Institute of Network Security and Trusted Computing,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
    4 School of Internet of Things,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
  • Received:2021-01-01 Revised:2021-01-25 Online:2021-06-15 Published:2021-06-03
  • About author:LIU Yu-tong,born in 1997,postgra-duate.Her main research interests include machine learning,transfer lear-ning and images processing.(
    LI Peng,born in 1979,Ph.D,professor,master supervisor,is a member of China Computer Federation.His main research interests include computer communication networks,cloud computing,and information security.
  • Supported by:
    National Natural Science Foundation of China(61872196,61872194,61902196),Scientific and Technological Support Project of Jiangsu Province(BE2019740),Natural Science Research Projects in Colleges and Universities of Jiangsu Province(18KJA520008,20KJB520001),Natural Science Foundation of Jiangsu Province(BK20200753) and Six Talent Peaks Project of Jiangsu Province(RJFW-111).

Abstract: Compared with the traditional image recognition methods,the depth network can extract the features with better representational ability,so as to obtain better recognition effect.In reality,most of the data provided by tasks are unlabeled or partially labeled,which makes it difficult for deep network to learn.The knowledge learned from the source domain is used for the learning of the target domain by means of transfer learning,which can alleviate this problem.In order to overcome the image-data diffe-rence between the source domain and the target domain in the transfer process,an image recognition method based on deep dyna-mic joint adaptation networks is proposed.During the training of the transfer networks,the dynamic joint adaptation method is used to realize the data distribution adaptation in the multi-layer network structure.Then the entropy minimization principle is used for the target classifier to pass through the low-density area of the target domain.At last,the image classification and recognition are realized.The experimental results show that,with this method,the average accuracy of the three transfer tasks based on 24 kinds of plant disease provided by the 2018 AI challenge competition are 97.27%,94.25% and 93.66%,which are better than other algorithms.A large number of empirical results show that the transfer learning framework based on the deep networks,meanwhile,using dynamic joint adaptation and entropy minimization principle can recognize images accurately.

Key words: Convolutional neural network, Deep learning, Domain adaption, Plant disease, Transfer learning

CLC Number: 

  • TP391.41
[1]DONAHUE J,JIA Y,VINYALS O,et al.DeCAF:A Deep Convolutional Activation Feature for Generic Visual Recognition[C]//International Conference on Machine Learning.2013:647-655.
[2]XAVIER G,BORDES A,BENGIO Y.Domain Adaptation for Large-Scale Sentiment Classification:A Deep Learning Approach[C]//Proceedings of International Conference on Machine Learning.Morgan Kaufmann Publishers Inc.,2011:513-520.
[3]TZENG E,HOFFMAN J,ZHANG N,et al.Deep Domain Confusion:Maximizing for Domain Invariance[J].arXiv:1412.3474,2014.
[4]YOSINSKI J,CLUNE J,BENGIO Y,et al.How transferableare features in deep neural networks?[C]//International Conference on Neural Information Processing Systems.Curran Associates Inc.,2014:3320-3328.
[5]LONG M,CAO Y,WANG J,et al.Learning Transferable Features with Deep Adaptation Networks[C]//Proceedings of International Conference on Machine Learning.Morgan Kaufmann Publishers Inc.,2015:97-105.
[6]LONG M,ZHU H,WANG J,et al.Deep Transfer Learningwith Joint Adaptation Networks[C]//International Conference on Machine Learning.2017:2208-2217.
[7]HE K,ZHANG X,REN S,et al.Deep Residual Learning forImage Recognition[C]//Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2016:770-778.
[8]GRANDVALET Y,BENGIO Y O.Semi-supervised Learning by Entropy Minimization[C]//Proceedings of Advances in Neural Information Processing Systems.MIT Press,2004:529-536.
[9]BALAKRISHNAN S,PONTIL M,FUKUMIZU K.Optimalkernel choice for large-scale two-sample tests[C]//Advances in Neural Information Processing Systems.2012:1205-1213.
[10]TAN C,SUN F,KONG T,et al.A Survey on Deep TransferLearning[J].arXiv:1808.01974,2018.
[11]SAENKO K,KULIS B,FRITZ M,et al.Adapting Visual Category Models to New Domains[M].Berlin,Heidelberg:Springer,2010:213-226.
[12]COLLOBERT R,WESTON J,BOTTOU L,et al.Natural Language Processing (almost) from Scratch[J].Journal of Machine Learning Research,2011,12:2493-2537.
[13]GHIFARY M,KLEIJN W B,ZHANG M.Domain AdaptiveNeural Networks for Object Recognition[C]//Proceedings of Pacific Rim International Conference on Artificial Intelligence.Berlin, Heidelberg:Springer,2014:898-904.
[14]BORGWARDT K M,GRETTON A,RASCH M J,et al.In-tegrating structured biological data by Kernel Maximum Mean Discrepancy[J].Bioinformatics,2006,22(14):49-57.
[15]LONG M,ZHU H,WANG J,et al.Unsupervised Domain Adaptation with Residual Transfer Networks[C]//Proceedings of Advances in Neural Information Processing Systems.MIT Press,2016:136-144.
[16]SUN B,SAENKO K.Deep CORAL:Correlation Alignment for Deep Domain Adaptation[C]//Proceedings of European Confe-rence on Computer Vision.Berlin Heidelberg:Springer,2016:443-450.
[17]ZELLINGER W,GRUBINGER T,LUGHOFER E,et al.Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning[J].arXiv:1702.08811,2017.
[18]GOODFELLOW I J,POUGET-ABADIE J,MIRZA M,et al.Generative Adversarial Nets[C]//Advances in Neural Information Processing Systems.2014:2672-2680.
[19]GANIN Y,USTINOVA E,AJAKAN H,et al.Domain-Adversarial Training of Neural Networks[J].The Journal of Machine Learning Research,2016,17(1):2096-3030.
[20]CAO Z,LONG M,WANG J,et al.Partial Transfer Learning with Selective Adversarial Networks[J].arXiv:1707.07901,2017.
[21]BEN-DAVID S,BLITZER J,CRAMMER K,et al.A theory of learning from different domains[J].Machine Learning,2010,79(1/2):151-175.
[22]PAN S J,TSANG I W,KWOK J T,et al.Domain adaptation via transfer component analysis[J].IEEE Transactions on Neural Networks,2011,22(2):199-210.
[23]LONG M,WANG J,DING G,et al.Transfer Joint Matching for Unsupervised Domain Adaptation[C]//IEEE Conference on Computer Vision and Pattern Recognition.IEEE,2014:1410-1417.
[24]LONG M,WANG J,DING G,et al.Transfer Feature Learning with Joint Distribution Adaptation[C]//Proceedings of the 2013 IEEE International Conference on Computer Vision.IEEE,2013.
[25]WANG J,CHEN Y,HAO S,et al.Balanced Distribution Adaptation for Transfer Learning[C]//The IEEE International Conference on Data Mining.IEEE,2017:1129-1134.
[26]WANG J,FENG W,CHEN Y,et al.Visual Domain Adaptation with Manifold Embedded Distribution Alignment[C]//2018 ACM Multimedia Conference on Multimedia Conference.ACM,2018:402-410.
[27]BEN-DAVID S,BLITZER J,CRAMMER K,et al.Analysis of Representations for Domain Adaptation[C]//International Conference on Neural Information Processing Systems.MIT Press,2007:137-144.
[28]VAN DER M,HINTON G.Visualizing data using t-SNE[J].Journal of Machine Learning Research,2008,9(2605):2579-2605.
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