[1] Yang G, Chen K X, Zhou M Y et al. Study evolution of detection and recognition on target in SAR image[J]. Progress in Geophysics, 22, 617-621(2007).

[2] Hinton G E, Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 313, 504-507(2006). http://logcom.oxfordjournals.org/cgi/ijlink?linkType=ABST&journalCode=sci&resid=313/5786/504

[3] Krizhevsky A, Sutskever I, Hinton G E et al. ImageNet classification with deep convolutional neural networks[C]. //Proceedings of the 25th Interference Conference on Neural Information Processing Systems, December 5-8, 2013, Lake Tahoe, Nevada, USA. New York: Curran Associates, 1, 1097-1105(2012).

[4] Bengio Y, Lamblin P, Popovici D et al. Greedy layer-wise training of deep networks[C]. //Proceedings of the 19th International Conference on Neural Information Processing Systems, December 4-7, 2006, Vancouver, British Columbia, Canada., 153-160(2006).

[5] Le Cun Y, Bottou L, Bengio Y et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 86, 2278-2324(1998). http://brain.oxfordjournals.org/lookup/external-ref?access_num=10.1109/5.726791&link_type=DOI

[6] Goodfellow I, Pouget-Abadie J, Mirza M et al. Generative adversarial networks[J]. Communications of the ACM, 63, 2672-2680(2020).

[7] Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2015-04-10)[2020-08-23]. https://arxiv.org/abs/1409.1556

[8] He K M, Zhang X Y, Ren S Q et al. Deep residual learning for image recognition[C]. //2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA., 770-778(2016).

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

[10] Ioffe S, Szegedy C. Batch normalization: accelerating deep network training by reducing internal covariate shift[EB/OL]. (2015-03-02)[2020-08-23]. https://arxiv.org/abs/1502.03167

[11] Szegedy C, Ioffe S, Vanhoucke V et al. Inception-v4, Inception-ResNet and the impact of residual connections on learning[EB/OL]. (2016-08-23)[2020-08-23]. https://arxiv.org/abs/1602.07261

[12] Jaderberg M, Simonyan K, Zisserman A et al. Spatial transformer networks[C]. //Proceedings of the 28th International Conference on Neural Information Processing Systems, December 7-12, 2015, Montreal, Quebec, Canada. New York: Curran Associates, 2, 2017-2025(2015).

[13] Hu J, Shen L, Albanie S et al. Squeeze-and-excitation networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, 2011-2023(2020).

[14] Wang F, Jiang M Q, Qian C et al. Residual attention network for image classification[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 6450-6458(2017).

[15] Xie S N, Girshick R, Dollár P et al. Aggregated residual transformations for deep neural networks[C]. //2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA., 5987-5995(2017).

[16] Wang X L, Girshick R, Gupta A et al. Non-local neural networks[C]. //2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA., 7794-7803(2018).

[17] Woo S, Park J, Lee J Y et al. CBAM: convolutional block attention module[M]. //Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer, 11211, 3-19(2018).

[18] Fu J, Liu J, Tian H J et al. Dual attention network for scene segmentation[C]. //2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA., 3141-3149(2019).

[19] Ross T D, Worrell S W, Velten V J et al. Standard SAR ATR evaluation experiments using the MSTAR public release data set[J]. Proceedings of SPIE, 3370, 566-573(1998).

[20] Duchi J, Hazan E, Singer Y. Adaptive subgradient methods for online learning and stochastic optimization[J]. Journal of Machine Learning Research, 12, 257-269(2011).

[21] Kingma D P, Ba J. Adam: a method for stochastic optimization[EB/OL]. (2017-01-30)[2020-08-23]. https://arxiv.org/abs/1412.6980

[22] Chen S Z, Wang H P, Xu F et al. Target classification using the deep convolutional networks for SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 54, 4806-4817(2016).

[23] Zhao M H, Zhong S S, Fu X Y et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 16, 4681-4690(2020). http://ieeexplore.ieee.org/document/8850096

[24] Zhang N, Donahue J, Girshick R et al. Part-based R-CNNs for fine-grained category detection[M]. //Fleet D, Pajdla T, Schiele B, et al. Computer vision-ECCV 2014. Lecture notes in computer science. Cham: Springer, 8689, 834-849(2014).

[25] Hu X, Yao Q L, Hou B Q et al. Target recognition using convolution neural network for SAR images[J]. Science Technology and Engineering, 19, 228-232(2019).

[26] Wang L, Qin W M, Luo W. SAR target image recognition and classification based on multitask learning and faster region-based convolution neural network[J]. Science Technology and Engineering, 17, 82-86(2017).

[27] Zhao Q, Principe J C. Support vector machines for SAR automatic target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 37, 643-654(2001).