• Acta Optica Sinica
  • Vol. 40, Issue 8, 0811005 (2020)
Lu Jin1、2、3, Shijian Liu1、3, Xiao Wang1、2、3, and Fanming Li1、3、*
Author Affiliations
  • 1Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
  • 2University of Chinese Academy of Sciences, Beijing 100049, China
  • 3Key Laboratory of Infrared System Detection and Imaging Technology, Chinese Academy of Sciences, Shanghai 200083, China
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    DOI: 10.3788/AOS202040.0811005 Cite this Article Set citation alerts
    Lu Jin, Shijian Liu, Xiao Wang, Fanming Li. Infrared Aircraft Classification Method with Small Samples Based on Improved Relation Network[J]. Acta Optica Sinica, 2020, 40(8): 0811005 Copy Citation Text show less
    References

    [1] Xie J R, Li F M, Wei H et al. Enhancement of single shot multibox detector for aerial infrared target detection[J]. Acta Optica Sinica, 39, 0615001(2019).

    [2] Ratner A J, Ehrenberg H, Hussain Z et al. Learning to compose domain-specific transformations for data augmentation. [C]∥Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, California. New York: Curran Associates, 3236-3246(2017).

    [3] Kulkarni T D, Whitney W F, Kohli P et al. Deep convolutional inverse graphics network. [C]∥Neural Information Processing Systems 2015, December 7-12, 2015, Montreal, QC, Canada. New York: Curran Associates, 2539-2547(2015).

    [4] Shorten C, Khoshgoftaar T M. A survey on image data augmentation for deep learning[J]. Journal of Big Data, 6, 60(2019).

    [5] Shrivastava A, Pfister T, Tuzel O et al. Learning from simulated and unsupervised images through adversarial training. [C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI. New York: IEEE, 2107-2116(2017).

    [6] Antoniou A, Storkey A. -03-21)[2019-12-03]. https:∥arxiv.xilesou., top/abs/1711, 04340(2018).

    [7] Xie J R, Li F M, Wei H et al. Infrared target simulation method based on generative adversarial neural networks[J]. Acta Optica Sinica, 39, 0311002(2019).

    [8] Li F F, Fergus R, Perona P. One-shot learning of object categories[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28, 594-611(2006).

    [9] Jia X, Zhang J L, Wen X B. Infrared faults recognition for electrical equipments based on dual supervision signals deep learning[J]. Infrared and Laser Engineering, 47, 0703003(2018).

    [10] Khosravan N, Bagci U. S4ND: single-shot single-scale lung nodule detection[M]. ∥Frangi A, Schnabel J, Davatzikos C, et al. Medical image computing and computer assisted intervention-MICCAI 2018. Lecture notes in computer science. Cham: Springer, 11071, 794-802(2018).

    [11] Jamal M A, Qi G J. Task agnostic meta-learning for few-shot learning. [C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 16-20, 2019, Long Beach, California. New York: IEEE, 11719-11727(2019).

    [12] Sun Q R, Liu Y Y, Chua T S et al. Meta-transfer learning for few-shot learning. [C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 16-20, 2019, Long Beach, California. New York: IEEE, 403-412(2019).

    [13] Vinyals O, Blundell C, Lillicrap T et al. Matching networks for one shot learning. [C]∥Neural Information Processing Systems 2016, December 5-10, 2016, Barcelona, Spain. New York: Curran Associates, 3630-3638(2016).

    [14] Snell J, Swersky K, Zemel R. Prototypical networks for few-shot learning. [C]∥Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, California. New York: Curran Associates, 4077-4087(2017).

    [15] Sung F, Yang Y X, Zhang L et al. Learning to compare: relation network for few-shot learning. [C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 18-22, 2018, Salt Lake, UT, USA. New York: IEEE, 1199-1208(2018).

    [16] Szegedy C, Liu W, Jia Y Q et al. Going deeper with convolutions. [C]∥The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 1-9(2015).

    [17] Kingma D P. -01-30)[2019-12-03]. https:∥arxiv.xilesou., top/abs/1412, 6980(2017).

    [18] Banerjee A, Merugu S, Dhillon I S et al. Clustering with Bregman divergences[J]. Journal of Machine Learning Research, 6, 1705-1749(2005).

    [19] Ravi S, Larochelle H. Optimization as a model for few-shot learning. [C]∥International Conference on Learning Representations, April 24-26, 2017, Toulon, France. [S.l.: s.n.](2017).

    [20] Chen W Y, Liu Y C, Kira Z et al. -01-12)[2019-12-03]. https:∥arxiv.xilesou., top/abs/1904, 04232(2020).

    [21] Finn C, Abbeel P, Levine S. Model-agnostic meta-learning for fast adaptation of deep networks. [C]∥Proceedings of the 34th International Conference on Machine Learning, August 6-11, 2017, Sydney, NSW, Australia. [S.l.: s.n.], 70, 1126-1135(2017).

    [22] Ben-David S, Blitzer J, Crammer K et al. Analysis of representations for domain adaptation. [C]∥Neural Information Processing Systems 2006, December 4-7, 2006, Vancouver, BC, Canada. New York: Curran Associates, 137-144(2007).

    [23] Wang M, Deng W H. Deep visual domain adaptation: a survey[J]. Neurocomputing, 312, 135-153(2018).

    [24] Zeiler M D, Fergus R. Visualizing and understanding convolutional networks[M]. ∥Fleet D, Pajdla T, Schiele B, et al. Computer Vision-ECCV 2014. Lecture Notes in Computer Science. Cham: Springer, 8689, 818-833(2014).

    Lu Jin, Shijian Liu, Xiao Wang, Fanming Li. Infrared Aircraft Classification Method with Small Samples Based on Improved Relation Network[J]. Acta Optica Sinica, 2020, 40(8): 0811005
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