[1] Wang Z W, She Q, Ward T E[2020-01-31]. Generative adversarial networks: a survey and taxonomy [2020-01-31].https:∥arxiv., org/abs/1701, 04862.
[4] Hammer H, Schulz K. Coherent simulation of SAR images[J]. Proceedings of SPIE, 7477, 74771G(2009).
[5] Cubuk E D, Zoph B, Mané D et al. AutoAugment: learning augmentation strategies from data. [C]∥2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 15-20, 2019, Long Beach, CA, USA. New York: IEEE, 113-123(2019).
[6] Goodfellow I J, Pouget-Abadie J, Mirza M et al[2020-01-27]. Generative adversarial nets [2020-01-27].https:∥arxiv., org/abs/1406, 2661.
[7] Arjovsky M, Bottou L[2020-02-05]. Towards principled methods for training generative adversarial networks [2020-02-05].https:∥arxiv., org/abs/1701, 04862.
[8] Mirza M, Osindero S[2020-01-30]. Conditional generative adversarial nets [2020-01-30].https:∥arxiv., org/abs/1411, 1784.
[9] Chen X, Duan Y, Houthooft R et al[2020-02-03]. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets [2020-02-03].https:∥arxiv., org/abs/1606, 03657.
[10] Isola P, Zhu J Y, Zhou T H et al. Image-to-image translation with conditional adversarial networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 5967-5976(2017).
[11] Zhu J Y, Park T, Isola P et al. Unpaired image-to-image translation using cycle-consistent adversarial networks. [C]∥2017 IEEE International Conference on Computer Vision (ICCV), October 22-29, 2017, Venice, Italy. New York: IEEE, 2242-2251(2017).
[12] Choi Y, Choi M, Kim M et al. StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. [C]∥2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 8789-8797(2018).
[13] Ajocsky M, Chintala S, Bottou L[2020-02-04]. Wasserstein GAN [2020-02-04].https:∥arxiv., org/abs/1701, 07875.
[15] Cui Z Y, Zhang M R, Cao Z J et al. Image data augmentation for SAR sensor via generative adversarial nets[J]. IEEE Access, 7, 42255-42268(2019).
[16] Shaham T R, Dekel T, Michaeli T. SinGAN: learning a generative model from a single natural image. [C]∥2019 IEEE/CVF International Conference on Computer Vision (ICCV), October 27-November 2, 2019, Seoul, Korea (South). New York: IEEE, 4569-4579(2019).
[17] Szegedy C, Vanhoucke V, Ioffe S et al. Rethinking the inception architecture for computer vision. [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2818-2826(2016).
[18] Wang X T, Yu K, Wu S X et al. ESRGAN: enhanced super-resolution generative adversarial networks[M]. ∥ Leal-Taixé L, Roth S, et al. Computer Vision -ECCV 2018. Lecture Notes in Computer Science. Cham: Springer, 11133, 63-79(2018).
[19] Huang G. Liu Z, van der Maaten L, et al. Densely connected convolutional networks. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2261-2269(2017).
[20] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution. [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 1132-1140(2017).
[21] Szegedy C, Ioffe S, Vanhoucke V et al[2020-01-30]. Inception-v4, inception-ResNet and the impact of residual connections on learning [2020-01-30].https:∥arxiv., org/abs/1602, 07261.
[22] Li J W, Qu C W, Peng S J et al. Ship detection in SAR images based on generative adversarial network and online hard examples mining[J]. Journal of Electronics & Information Technology, 41, 143-149(2019).