Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (2): 217-223.doi: 10.11896/jsjkx.200700028

• Artificial Intelligence • Previous Articles     Next Articles

Unsupervised Domain Adaptation Based on Weighting Dual Biases

MA Chuang1, TIAN Qing1,2, SUN He-yang1, CAO Meng1, MA Ting-huai1   

  1. 1 School of Computer and Software,Nanjing University of Information Science & Technology,Nanjing 210044,China
    2 National Laboratory of Pattern Recognition,Institue of Automation,Chinese Academy of Sciences,Beijing 100190,China
  • Received:2020-07-06 Revised:2020-08-15 Online:2021-02-15 Published:2021-02-04
  • About author:MA Chuang,born in 1996,postgraduate.His main research interests include machine learning and pattern recognition.
    TIAN Qing,born in 1984,Ph.D,asso-ciate professor,postgraduate supervisor.His main research interests include machine learning and pattern recognition.
  • Supported by:
    The National Natural Science Foundation of China(61702273),Natural Science Foundation of Jiangsu Province(BK20170956),Natural Science Foundation of the Jiangsu Higher Education Institutions of China(17KJB520022),Open Projects Program of Chinese Academy of Science National Laboratory of Pattern Recognition(202000007) and Open Project Program of MIIT Key Laboratory of Pattern Analysis and Machine Intelligence(NJ2019010).

PDF (PC)

657

Abstract: Unsupervised domain adaptation (UDA) is a new kind of machine learning paradigm,which facilitates the training of target domain model through transferring knowledge from source domain to unlabeled target domain.In order to model the domain distribution difference between the source domain and target domain,the maximum mean discrepancy (MMD) is widely applied,it plays an effective role in promoting the performance of UDA.Usually,the class size and data distribution between the target domain and the source domain are not the same,unfortunately,these methods usually ignore this structure information.To this end,this paper proposes a model called sample weighted and class weighted based unsupervised domain adaptation network (SCUDAN).On one hand,the class distribution alignment between the source domain and the target domain is achieved through adaptive weighting on the classes of the source domain.On the other hand,the class centers between the target domain and the source domain can be aligned through adaptive weighting on the samples of the target domain.In addition,a CEM (Classification Expectation Maximization) algorithm is proposed to optimize SCUDAN.Finally,the effectiveness of the proposed method is verified by comparative experiments and analysis.

Key words: Class weight bias, Convolutional neural network, Maximum mean discrepancy, Sample weight bias, Unsupervised domain adaptation

CLC Number: 

  • TP391
[1] LAWRENCE S,GILES C L,TSOI A C,et al.Face recognition:A convolutional neural-network approach[J].IEEE Transactions on Neural Networks,1997,8(1):98-113.
[2] PAN S J,TSANG I W,KWOK J T,et al.Domain adaptation via transfer component analysis[J].IEEE Transactions on Neural Networks,2010,22(2):199-210.
[3] DAUMÉ III H.Frustratingly easy domain adaptation[J].arXiv:0907.1815,2009.
[4] SUN D M,ZHANG F F,MAO Q R.Label-Guided Domain Adaptation Method in Generative Adversarial Network for Facial Expression Recognition[J].Computer Engineering,2020,46(5):267-273,281.
[5] WANG G,HAN H,SHAN S,et al.Unsupervised Adversarial Domain Adaptation for Cross-Domain Face Presentation Attack Detection[J].IEEE Transactions on Information Forensics and Security,2020,16:56-69.
[6] DUAN J M,MA X Z,LIU X.Path Tracking Method of Unmanned Vehicle Based on MFAPC[J].Computer Engineering,2019,45(6):6-11.
[7] INOUE N,FURUTA R,YAMASAKI T,et al.Cross-domainweakly-supervised object detection through progressive domain adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:5001-5009.
[8] CHEN C,GUO W B,LI Q Y.Adversarial domain adaptationwith self-attention in image classification[J].Computer Engineering and Science,2020,42(2):259-265.
[9] SAITO K,KIM D,SCLAROFF S,et al.Semi-supervised domain adaptation via minimax entropy[C]//Proceedings of the IEEE International Conference on Computer Vision.2019:8050-8058.
[10] DAUMÉ III H,KUMAR A,SAHA A.Frustratingly easy semi-supervised domain adaptation[C]//Proceedings of the 2010 Workshop on Domain Adaptation for Natural Language Processing.Association for Computational Linguistics,2010:53-59.
[11] KUMAR A,SAHA A,DAUME H.Co-regularization basedsemi-supervised domain adaptation[C]//Advances in Neural Information Processing Systems.2010:478-486.
[12] FERNANDO B,HABRARD A,SEBBAN M,et al.Unsuper-vised visual domain adaptation using subspace alignment[C]//Proceedings of the IEEE International Conference on Computer Vision.2013:2960-2967.
[13] GANIN Y,LEMPITSKY V.Unsupervised domain adaptationby backpropagation[J].arXiv:1409.7495,2014.
[14] JIANG J,ZHAI C X.Instance weighting for domain adaptation in NLP[C]//Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics.2007:264-271.
[15] CHEN Q,LIU Y,WANG Z,et al.Re-weighted adversarial adaptation network for unsupervised domain adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:7976-7985.
[16] LIU J,ZHANG L.Optimal projection guided transfer hashingfor image retrieval[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2019,33:8754-8761.
[17] LONG M,WANG J,DING G,et al.Transfer feature learningwith joint distribution adaptation[C]//Proceedings of the IEEE International Conference on Computer Vision.2013:2200-2207.
[18] DUAN L,XU D,TSANG I W H,et al.Visual event recognition in videos by learning from web data[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,34(9):1667-1680.
[19] CAO Y,LONG M,WANG J.Unsupervised domain adaptation with distribution matching machines[C]//Thirty-Second AAAI Conference on Artificial Intelligence.2018.
[20] LONG M,CAO Y,CAO Z,et al.Transferable representation learning with deep adaptation networks[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2018,41(12):3071-3085.
[21] LONG M,ZHU H,WANG J,et al.Deep transfer learning with joint adaptation networks[C]//Proceedings of the 34th International Conference on Machine Learning-Volume 70.2017:2208-2217.
[22] PINHEIRO P O.Unsupervised domain adaptation with similarity learning[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:8004-8013.
[23] ZHANG J,DING Z,LI W,et al.Importance weighted adversa-rial nets for partial domain adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2018:8156-8164.
[24] DZIUGAITE G K,ROY D M,GHAHRAMANI Z.Traininggenerative neural networks via maximum mean discrepancy optimization[J].arXiv:1505.03906,2015.
[25] GONG B,GRAUMAN K,SHA F.Reshaping visual datasets for domain adaptation[C]//Advances in Neural Information Processing Systems.2013:1286-1294.
[26] MOTIIAN S,PICCIRILLI M,ADJEROH D A,et al.Unifieddeep supervised domain adaptation and generalization[C]//Proceedings of the IEEE International Conference on Computer Vision.2017:5715-5725.
[27] KANG G,JIANG L,YANG Y,et al.Contrastive adaptation network for unsupervised domain adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2019:4893-4902.
[28] WANG J,CHEN Y,HAO S,et al.Balanced distribution adaptation for transfer learning[C]//2017 IEEE InternationalConfe-rence on Data Mining (ICDM).IEEE,2017:1129-1134.
[29] CELEUX G,GOVAERT G.A classification EM algorithm for clustering and two stochastic versions[J].Computational statistics & Data analysis,1992,14(3):315-332.
[30] AL-JAWFI R.Handwriting Arabic character recognition LeNet using neural network[J].International Arab Journal of Information Technology,2009,6(3):304-309.
[31] IANDOLA F N,HAN S,MOSKEWICZ M W,et al.Squeeze-Net:AlexNet-level accuracy with 50x fewer parameters and< 0.5 MB model size[J].arXiv:1602.07360,2016.
[32] ZHONG Z,JIN L,XIE Z.High performance offline handwritten chinese character recognition using googlenet and directional feature maps[C]//2015 13th International Conference on Document Analysis and Recognition (ICDAR).IEEE,2015:846-850.
[33] SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[34] LONG M,CAO Y,WANG J,et al.Learning transferable fea-tures with deep adaptation networks[J].arXiv:1502.02791,2015.
[35] WESTON J,RATLE F,MOBAHI H,et al.Deep learning via semi-supervised embedding[M] ∥Neural networks:Tricks of the trade.Springer,Berlin,Heidelberg,2012:639-655.
[36] TZENG E,HOFFMAN J,ZHANG N,et al.Deep domain confusion:Maximizing for domain invariance[J].arXiv:1412.3474,2014.
[37] YAN H,DING Y,LI P,et al.Mind the class weight bias:Weighted maximum mean discrepancy for unsupervised domain adaptation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.2017:2272-2281.
[1] ZHOU Le-yuan, ZHANG Jian-hua, YUAN Tian-tian, CHEN Sheng-yong. Sequence-to-Sequence Chinese Continuous Sign Language Recognition and Translation with Multi- layer Attention Mechanism Fusion [J]. Computer Science, 2022, 49(9): 155-161.
[2] CHEN Yong-quan, JIANG Ying. Analysis Method of APP User Behavior Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(8): 78-85.
[3] ZHU Cheng-zhang, HUANG Jia-er, XIAO Ya-long, WANG Han, ZOU Bei-ji. Deep Hash Retrieval Algorithm for Medical Images Based on Attention Mechanism [J]. Computer Science, 2022, 49(8): 113-119.
[4] DAI Zhao-xia, LI Jin-xin, ZHANG Xiang-dong, XU Xu, MEI Lin, ZHANG Liang. Super-resolution Reconstruction of MRI Based on DNGAN [J]. Computer Science, 2022, 49(7): 113-119.
[5] LIU Yue-hong, NIU Shao-hua, SHEN Xian-hao. Virtual Reality Video Intraframe Prediction Coding Based on Convolutional Neural Network [J]. Computer Science, 2022, 49(7): 127-131.
[6] XU Ming-ke, ZHANG Fan. Head Fusion:A Method to Improve Accuracy and Robustness of Speech Emotion Recognition [J]. Computer Science, 2022, 49(7): 132-141.
[7] ZHANG Jia-hao, LIU Feng, QI Jia-yin. Lightweight Micro-expression Recognition Architecture Based on Bottleneck Transformer [J]. Computer Science, 2022, 49(6A): 370-377.
[8] WANG Jian-ming, CHEN Xiang-yu, YANG Zi-zhong, SHI Chen-yang, ZHANG Yu-hang, QIAN Zheng-kun. Influence of Different Data Augmentation Methods on Model Recognition Accuracy [J]. Computer Science, 2022, 49(6A): 418-423.
[9] SUN Jie-qi, LI Ya-feng, ZHANG Wen-bo, LIU Peng-hui. Dual-field Feature Fusion Deep Convolutional Neural Network Based on Discrete Wavelet Transformation [J]. Computer Science, 2022, 49(6A): 434-440.
[10] YANG Yue, FENG Tao, LIANG Hong, YANG Yang. Image Arbitrary Style Transfer via Criss-cross Attention [J]. Computer Science, 2022, 49(6A): 345-352.
[11] YANG Jian-nan, ZHANG Fan. Classification Method for Small Crops Combining Dual Attention Mechanisms and Hierarchical Network Structure [J]. Computer Science, 2022, 49(6A): 353-357.
[12] WU Zi-bin, YAN Qiao. Projected Gradient Descent Algorithm with Momentum [J]. Computer Science, 2022, 49(6A): 178-183.
[13] ZHAO Zheng-peng, LI Jun-gang, PU Yuan-yuan. Low-light Image Enhancement Based on Retinex Theory by Convolutional Neural Network [J]. Computer Science, 2022, 49(6): 199-209.
[14] HU Fu-yuan, WAN Xin-jun, SHEN Ming-fei, XU Jiang-lang, YAO Rui, TAO Zhong-ben. Survey Progress on Image Instance Segmentation Methods of Deep Convolutional Neural Network [J]. Computer Science, 2022, 49(5): 10-24.
[15] XU Hua-chi, SHI Dian-xi, CUI Yu-ning, JING Luo-xi, LIU Cong. Time Information Integration Network for Event Cameras [J]. Computer Science, 2022, 49(5): 43-49.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.