·1070· 智能系统学报 第14卷 ation[J].IEEE transactions on information theory,1967, [16]MOVSHOVITZ-ATTIAS Y.TOSHEV A.LEUNG T K 131):21-27 et al.No fuss distance metric learning using proxies[C]// [4]SUAREZ J L.GARCIA S.HERRERA F.A tutorial on dis- Proceedings of the IEEE International Conference on tance metric learning:mathematical foundations,al- Computer Vision.Venice,Italy,2017:360-368. gorithms and software[J].arXiv preprint arXiv:1812. [17]HERSHEY J R,CHEN Zhuo,LE ROUX J,et al.Deep clustering:Discriminative embeddings for segmentation 05944.2018. and separation[Cl//2016 IEEE International Conference [5]WEINBERGER K Q,SAUL L K.Distance metric learn- on Acoustics,Speech and Signal Processing.Shanghai, ing for large margin nearest neighbor classification[J]. China,2016:31-35. Journal of machine learning research,2009,10:207-244. [18]SONG H O,XIANG Yu,JEGELKA S,et al.Deep metric [6]GOLDBERGER J,ROWEIS S,HINTON G,et al.Neigh- learning via lifted structured feature embedding[C]//Pro- bourhood components analysis[C]//Proceedings of the 17th ceedings of the IEEE Conference on Computer Vision International Conference on Neural Information Pro- and Pattern Recognition.Las Vegas,USA,2016: cessing Systems.Vancouver,British Columbia,Canada, 4004-4012. 2004:513-520. [19]SENER O.SONG H O,SAXENA A,et al.Learning transferrable representations for unsupervised domain ad- [7]VAN DER MAATEN L,POSTMA E,VAN DEN HERIK aptation[C]//Proceedings of the 30th Conference on Neur- J.Dimensionality reduction:a comparative[J].Journal of al Information Processing Systems.Barcelona,Spain, machine learning research,2009,10:66-71. 2016:2110-2118. [8]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Im- [20]BROMLEY J.GUYON I,LECUN Y,et al.Signature agenet classification with deep convolutional neural net- verification using a "siamese"time delay neural network works[Cl//Proceedings of the 25th International Confer- [C]//Proceedings of the 6th International Conference on ence on Neural Information Processing Systems.Lake Neural Information Processing Systems.Denver,USA, Tahoe,USA,2012:1097-1105 1993:737-744 [9]SIMONYAN K,ZISSERMAN A.Very deep convolution- [21]CHOY C B,GWAK J,SAVARESE S,et al.Universal correspondence network[C//Proceedings of the 30th Con- al networks for large-scale image recognition[J].ar Xiv pre- ference on Neural Information Processing Systems.Bar- print arXiv:1409.1556.2014. celona,Spain,2016:2414-2422. [10]SZEGEDY C,LIU Wei,JIA Yangqing,et al.Going deep- [22]PRABHU Y.VARMA M.FastXML:A fast,accurate and er with convolutions[C]//Proceedings of the IEEE Con- stable tree-classifier for extreme multi-label learning[C]// ference on Computer Vision and Pattern Recognition.Bo- Proceedings of the 20th ACM SIGKDD International ston,USA,2015:1-9. Conference on Knowledge Discovery and Data Mining. [11]HE Kaiming,ZHANG Xiangyu,REN Shaoqing,et al. New York,USA,2014:263-272. Deep residual learning for image recognition[C]//Pro- [23]YEN I E H.HUANG Xiangru,ZHONG Kai,et al.PD- ceedings of the IEEE Conference on Computer Vision sparse:a primal and dual sparse approach to extreme mul- and Pattern Recognition.Las Vegas,USA,2016:770- ticlass and multilabel classification[Cl//Proceedings of the 778 33rd International Conference on International Confer- [12]HUANG Gao,LIU Zhuang,VAN DER MAATEN L,et ence on Machine Learning.New York,USA,2016: al.Densely connected convolutional networks[Cl//Pro- 3069-3077 ceedings of the IEEE Conference on Computer Vision [24]CHOROMANSKA A.AGARWAL A.LANGFORD J. and Pattern Recognition.Honolulu,USA,2017:4700- Extreme multi class classification[Cl//Neural Information 4708. Processing Systems Conference.Lake Tahoe,USA,2013. [13]HU Jie,SHEN Li,SUN Gang.Squeeze-and-excitation [25]SCHROFF F,KALENICHENKO D,PHILBIN J.Fa- networks[C]//Proceedings of the IEEE Conference on ceNet:A unified embedding for face recognition and clus- Computer Vision and Pattern Recognition.Salt Lake City, tering[Cl//Proceedings of the IEEE Conference on Com- USA.2018:7132-7141. puter Vision and Pattern Recognition.Boston,USA, [14]CHECHIK G,SHARMA V,SHALIT U,et al.Large 2015:815-823. scale online learning of image similarity through ranking [26]HADSELL R.CHOPRA S,LECUN Y.Dimensionality [J].Journal of machine learning research,2010,11: reduction by learning an invariant mapping[C]//2006 1109-1135. IEEE Computer Society Conference on Computer Vision [15]SOHN K.Improved deep metric learning with multi-class and Pattern Recognition.New York,USA,2006,2: n-pair loss objective[C]//Proceedings of the 39th Confer- 1735-1742 ence on Neural Information Processing Systems.Bar- [27]ZHAI A,WU Haoyu.Making classification competitive celona,Spain,2016:1857-1865. for deep metric learning[J].arXiv preprint arXiv:1811.ation[J]. IEEE transactions on information theory, 1967, 13(1): 21–27. SUÁREZ J L, GARCÍA S, HERRERA F. A tutorial on dis￾tance metric learning: mathematical foundations, al￾gorithms and software[J]. arXiv preprint arXiv: 1812. 05944, 2018. [4] WEINBERGER K Q, SAUL L K. Distance metric learn￾ing for large margin nearest neighbor classification[J]. Journal of machine learning research, 2009, 10: 207–244. [5] GOLDBERGER J, ROWEIS S, HINTON G, et al. Neigh￾bourhood components analysis[C]//Proceedings of the 17th International Conference on Neural Information Pro￾cessing Systems. Vancouver, British Columbia, Canada, 2004: 513–520. [6] VAN DER MAATEN L, POSTMA E, VAN DEN HERIK J. Dimensionality reduction: a comparative[J]. Journal of machine learning research, 2009, 10: 66–71. [7] KRIZHEVSKY A, SUTSKEVER I, HINTON G E. Im￾agenet classification with deep convolutional neural net￾works[C]//Proceedings of the 25th International Confer￾ence on Neural Information Processing Systems. Lake Tahoe, USA, 2012: 1097–1105. [8] SIMONYAN K, ZISSERMAN A. Very deep convolution￾al networks for large-scale image recognition[J]. arXiv pre￾print arXiv: 1409.1556, 2014. [9] SZEGEDY C, LIU Wei, JIA Yangqing, et al. Going deep￾er with convolutions[C] //Proceedings of the IEEE Con￾ference on Computer Vision and Pattern Recognition. Bo￾ston, USA, 2015: 1–9. [10] HE Kaiming, ZHANG Xiangyu, REN Shaoqing, et al. Deep residual learning for image recognition[C] //Pro￾ceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 770– 778. [11] HUANG Gao, LIU Zhuang, VAN DER MAATEN L, et al. Densely connected convolutional networks[C]//Pro￾ceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Honolulu, USA, 2017: 4700– 4708. [12] HU Jie, SHEN Li, SUN Gang. Squeeze-and-excitation networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Salt Lake City, USA, 2018: 7132–7141. [13] CHECHIK G, SHARMA V, SHALIT U, et al. Large scale online learning of image similarity through ranking [J]. Journal of machine learning research, 2010, 11: 1109–1135. [14] SOHN K. Improved deep metric learning with multi-class n-pair loss objective[C]//Proceedings of the 39th Confer￾ence on Neural Information Processing Systems. Bar￾celona, Spain, 2016: 1857–1865. [15] MOVSHOVITZ-ATTIAS Y, TOSHEV A, LEUNG T K, et al. No fuss distance metric learning using proxies[C]// Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy, 2017: 360–368. [16] HERSHEY J R, CHEN Zhuo, LE ROUX J, et al. Deep clustering: Discriminative embeddings for segmentation and separation[C]//2016 IEEE International Conference on Acoustics, Speech and Signal Processing. Shanghai, China, 2016: 31–35. [17] SONG H O, XIANG Yu, JEGELKA S, et al. Deep metric learning via lifted structured feature embedding[C]// Pro￾ceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 4004–4012. [18] SENER O, SONG H O, SAXENA A, et al. Learning transferrable representations for unsupervised domain ad￾aptation[C]//Proceedings of the 30th Conference on Neur￾al Information Processing Systems. Barcelona, Spain, 2016: 2110–2118. [19] BROMLEY J, GUYON I, LECUN Y, et al. Signature verification using a "siamese" time delay neural network [C]//Proceedings of the 6th International Conference on Neural Information Processing Systems. Denver, USA, 1993: 737–744. [20] CHOY C B, GWAK J, SAVARESE S, et al. Universal correspondence network[C]//Proceedings of the 30th Con￾ference on Neural Information Processing Systems. Bar￾celona, Spain, 2016: 2414–2422. [21] PRABHU Y, VARMA M. FastXML: A fast, accurate and stable tree-classifier for extreme multi-label learning[C]// Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York, USA, 2014: 263–272. [22] YEN I E H, HUANG Xiangru, ZHONG Kai, et al. PD￾sparse: a primal and dual sparse approach to extreme mul￾ticlass and multilabel classification[C]//Proceedings of the 33rd International Conference on International Confer￾ence on Machine Learning. New York, USA, 2016: 3069–3077. [23] CHOROMANSKA A, AGARWAL A, LANGFORD J. Extreme multi class classification[C]//Neural Information Processing Systems Conference. Lake Tahoe, USA, 2013. [24] SCHROFF F, KALENICHENKO D, PHILBIN J. Fa￾ceNet: A unified embedding for face recognition and clus￾tering[C]//Proceedings of the IEEE Conference on Com￾puter Vision and Pattern Recognition. Boston, USA, 2015: 815–823. [25] HADSELL R, CHOPRA S, LECUN Y. Dimensionality reduction by learning an invariant mapping[C]//2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA, 2006, 2: 1735–1742. [26] ZHAI A, WU Haoyu. Making classification competitive for deep metric learning[J]. arXiv preprint arXiv: 1811. [27] ·1070· 智 能 系 统 学 报 第 14 卷