第6期 刘冰，等：深度度量学习综述 ·1071· 12649.2018 between similar vehicles[C]//Proceedings of the IEEE [28]HINTON G.VINYALS O.DEAN J.Distilling the know- Conference on Computer Vision and Pattern Recognition. ledge in a neural network[J].arXiv preprint arXiv:1503. Las Vegas,USA,2016:2167-2175. 02531,2015 [42]LAW M T,URTASUN R,ZEMEL R S.Deep spectral [29]ZHANG Xu,YU F X,KARAMAN S,et al.Heated-up clustering learning[C]//Proceedings of the 34th Interna- softmax embedding[J].arXiv preprint arXiv:1809.04157, tional Conference on Machine Learning.Sydney,Aus- 2018. tralia2017:1985-1994. [30]CHOPRA S,HADSELL R,LECUN Y.Learning a simil- [43]FUKUNAGA K,NARENDRA P M.A branch and bound arity metric discriminatively,with application to face algorithm for computing k-nearest neighbors[J].IEEE verification[C]//2005 IEEE Computer Society Confer- transactions on computers,1975,C-24(7):750-753. ence on Computer Vision and Pattern Recognition.San [44]WEN Yandong,ZHANG Kaipeng,LI Zhifeng,et al.A Dieg0,USA,2005:539-546 discriminative feature learning approach for deep face re- [31]ROWEIS S T.SAUL L K.Nonlinear dimensionality re- cognition[C]//Proceedings of the 14th European Confer- duction by locally linear embedding[J].Science,2000, ence on Computer Vision.Amsterdam,The Netherlands, 290(5500)2323-2326. 2016:499-515. [32]DONOHO D L,GRIMES C E.Hessian eigenmaps:Loc- [45]SONG H O,JEGELKA S,RATHOD V,et al.Deep met- ally linear embedding techniques for high-dimensional ric learning via facility location[C]//Proceedings of the data[J].Proceedings of the national academy of sciences IEEE Conference on Computer Vision and Pattern Recog- of the United States of America,2003,100(10): nition.Honolulu,USA,2017:5382-5390. 5591-5596. [46]BELL S,BALA K.Learning visual similarity for product [33]JOLLIFFE I T.Principal component analysis[M].Berlin: design with convolutional neural networks[J.ACM trans- Springer,2011. actions on graphics (TOG),2015,34(4):98 [34]NOROUZI M.FLEET D J,SALAKHUTDINOV R. [47]KAUFMAN L.ROUSSEEUW P J.DODGE Y.Cluster- Hamming distance metric learning[C]//Proceedings of the ing by Means of Medoids[M]//Dodge Y.Statistical Data 25th International Conference on Neural Information Pro- Analysis Based on the LI-Norm and Related Methods. cessing Systems.Lake Tahoe,USA,2012:1061-1069. North-Holland:Elsevier,1987. [35]CUI Yin,ZHOU Feng,LIN Yuanging,et al.Fine-grained [48]LIN Hui,BILMES J A.Learning mixtures of submodular categorization and dataset bootstrapping using deep met- shells with application to document summarization[C]// ric learning with humans in the loop[C]//Proceedings of Proceedings of the Twenty-Eighth Conference on Uncer- the IEEE Conference on Computer Vision and Pattern Re- tainty in Artificial Intelligence.Catalina Island,USA. cognition.Las Vegas,USA,2016:1153-1162 2012:479-490. [36]MISHCHUK A,MISHKIN D,RADENOVIC F,et al. [49]TSCHIATSCHEK S,IYER R K,WEI Haochen,et al. Working hard to know your neighbor's margins:Local Learning mixtures of submodular functions for image col- descriptor learning loss[C]//Advances in Neural Informa- lection summarization[Cl//Proceedings of the 27th Inter- tion Processing Systems.Long Beach,USA,2017: national Conference on Neural Information Processing 4826-4837. Systems.Montreal,Canada.2014:1413-1421. [37]HARWOOD B,KUMAR B G,CARNEIRO G,et al. [50]EMERSON A E.Handbook of theoretical computer sci- Smart mining for deep metric learning[C]//Proceedings of ence[M].Amsterdam:Elsevier,1990. the IEEE International Conference on Computer Vision. [51]KNUTH D E.Postscript about NP-hard problems[J]. Venice,Italy,2017:2821-2829. ACM SIGACT news.1974.6(2):15-16. [38]YUAN Yuhui,YANG Kuiyuan,ZHANG Chao.Hard- [52]MANNING C D.RAGHAVAN P.SCHUTZE H.Intro- aware deeply cascaded embedding[Cl//Proceedings of the duction to information retrieval[M].New York:Cam- IEEE International Conference on Computer Vision. bridge University Press,2008. Venice,Italy,2017:814-823. [53]IONESCU C,VANTZOS O,SMINCHISESCU C.Train- [39]WU Chaoyuan,MANMATHA R,SMOLA A J,et al. ing deep networks with structured layers by matrix back- Sampling matters in deep embedding learning[C]//Pro- propagation[J].arXiv preprint arXiv:1509.07838.2015. ceedings of the IEEE International Conference on Com- [54]WANG Xinshao,HUA Yang,KODIROV E,et al. puter Vision.Venice,Italy,2017:2840-2848. Ranked list loss for deep metric learning[J].arXiv pre- [40]USTINOVA E,LEMPITSKY V.Learning deep embed- print arXiv:1903.03238,2019. dings with histogram loss[C]//Proceedings of the 30th [55]SHARIF RAZAVIAN A.AZIZPOUR H,SULLIVAN J. Conference on Neural Information Processing Systems. et al.CNN features off-the-shelf:an astounding baseline Barcelona,Spain,2016:4170-4178. for recognition[C]//Proceedings of the IEEE Conference [41]LIU Hongye,TIAN Yonghong,WANG Yaowei,et al. on Computer Vision and Pattern Recognition Workshops. Deep relative distance learning:Tell the difference Columbus,USA,2014:806-81312649, 2018. HINTON G, VINYALS O, DEAN J. Distilling the know￾ledge in a neural network[J]. arXiv preprint arXiv: 1503. 02531, 2015. [28] ZHANG Xu, YU F X, KARAMAN S, et al. Heated-up softmax embedding[J]. arXiv preprint arXiv: 1809.04157, 2018. [29] CHOPRA S, HADSELL R, LECUN Y. Learning a simil￾arity metric discriminatively, with application to face verification[C]//2005 IEEE Computer Society Confer￾ence on Computer Vision and Pattern Recognition. San Diego, USA, 2005: 539–546 [30] ROWEIS S T, SAUL L K. Nonlinear dimensionality re￾duction by locally linear embedding[J]. Science, 2000, 290(5500): 2323–2326. [31] DONOHO D L, GRIMES C E. Hessian eigenmaps: Loc￾ally linear embedding techniques for high- dimensional data[J]. Proceedings of the national academy of sciences of the United States of America, 2003, 100(10): 5591–5596. [32] JOLLIFFE I T. Principal component analysis[M]. Berlin: Springer, 2011. [33] NOROUZI M, FLEET D J, SALAKHUTDINOV R. Hamming distance metric learning[C]//Proceedings of the 25th International Conference on Neural Information Pro￾cessing Systems. Lake Tahoe, USA, 2012: 1061–1069. [34] CUI Yin, ZHOU Feng, LIN Yuanqing, et al. Fine-grained categorization and dataset bootstrapping using deep met￾ric learning with humans in the loop[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Re￾cognition. Las Vegas, USA, 2016: 1153–1162. [35] MISHCHUK A, MISHKIN D, RADENOVIC F, et al. Working hard to know your neighbor's margins: Local descriptor learning loss[C]//Advances in Neural Informa￾tion Processing Systems. Long Beach, USA, 2017: 4826–4837. [36] HARWOOD B, KUMAR B G, CARNEIRO G, et al. Smart mining for deep metric learning[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy, 2017: 2821–2829. [37] YUAN Yuhui, YANG Kuiyuan, ZHANG Chao. Hard￾aware deeply cascaded embedding[C]//Proceedings of the IEEE International Conference on Computer Vision. Venice, Italy, 2017: 814–823. [38] WU Chaoyuan, MANMATHA R, SMOLA A J, et al. Sampling matters in deep embedding learning[C]//Pro￾ceedings of the IEEE International Conference on Com￾puter Vision. Venice, Italy, 2017: 2840–2848. [39] USTINOVA E, LEMPITSKY V. Learning deep embed￾dings with histogram loss[C]//Proceedings of the 30th Conference on Neural Information Processing Systems. Barcelona, Spain, 2016: 4170–4178. [40] LIU Hongye, TIAN Yonghong, WANG Yaowei, et al. Deep relative distance learning: Tell the difference [41] between similar vehicles[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, USA, 2016: 2167–2175. LAW M T, URTASUN R, ZEMEL R S. Deep spectral clustering learning[C]//Proceedings of the 34th Interna￾tional Conference on Machine Learning. Sydney, Aus￾tralia, 2017: 1985–1994. [42] FUKUNAGA K, NARENDRA P M. A branch and bound algorithm for computing k-nearest neighbors[J]. IEEE transactions on computers, 1975, C-24(7): 750–753. [43] WEN Yandong, ZHANG Kaipeng, LI Zhifeng, et al. A discriminative feature learning approach for deep face re￾cognition[C]//Proceedings of the 14th European Confer￾ence on Computer Vision. Amsterdam, The Netherlands, 2016: 499–515. [44] SONG H O, JEGELKA S, RATHOD V, et al. Deep met￾ric learning via facility location[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recog￾nition. Honolulu, USA, 2017: 5382–5390. [45] BELL S, BALA K. Learning visual similarity for product design with convolutional neural networks[J]. ACM trans￾actions on graphics (TOG), 2015, 34(4): 98. [46] KAUFMAN L, ROUSSEEUW P J, DODGE Y. Cluster￾ing by Means of Medoids[M]//Dodge Y. Statistical Data Analysis Based on the L1-Norm and Related Methods. North-Holland: Elsevier, 1987. [47] LIN Hui, BILMES J A. Learning mixtures of submodular shells with application to document summarization[C]// Proceedings of the Twenty-Eighth Conference on Uncer￾tainty in Artificial Intelligence. Catalina Island, USA, 2012: 479–490. [48] TSCHIATSCHEK S, IYER R K, WEI Haochen, et al. Learning mixtures of submodular functions for image col￾lection summarization[C]//Proceedings of the 27th Inter￾national Conference on Neural Information Processing Systems. Montreal, Canada, 2014: 1413–1421. [49] EMERSON A E. Handbook of theoretical computer sci￾ence[M]. Amsterdam: Elsevier, 1990. [50] KNUTH D E. Postscript about NP-hard problems[J]. ACM SIGACT news, 1974, 6(2): 15–16. [51] MANNING C D, RAGHAVAN P, SCHÜTZE H. Intro￾duction to information retrieval[M]. New York: Cam￾bridge University Press, 2008. [52] IONESCU C, VANTZOS O, SMINCHISESCU C. Train￾ing deep networks with structured layers by matrix back￾propagation[J]. arXiv preprint arXiv: 1509.07838, 2015. [53] WANG Xinshao, HUA Yang, KODIROV E, et al. Ranked list loss for deep metric learning[J]. arXiv pre￾print arXiv: 1903.03238, 2019. [54] SHARIF RAZAVIAN A, AZIZPOUR H, SULLIVAN J, et al. CNN features off-the-shelf: an astounding baseline for recognition[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops. Columbus, USA, 2014: 806–813. [55] 第 6 期 刘冰，等：深度度量学习综述 ·1071·