基于残差结构的对抗式网络图像生成方法 下载: 1078次
颜贝, 张礼, 张建林, 徐智勇. 基于残差结构的对抗式网络图像生成方法[J]. 激光与光电子学进展, 2020, 57(18): 181504.
Bei Yan, Li Zhang, Jianlin Zhang, Zhiyong Xu. Image Generation Method for Adversarial Network Based on Residual Structure[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181504.
[1] LeCun Y, Bengio Y, Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
[2] Salamon J, Bello J P. Deep convolutional neural networks and data augmentation for environmental sound classification[J]. IEEE Signal Processing Letters, 2017, 24(3): 279-283.
[3] Wang K F, Zuo W M, Tan Y, et al. Generative adversarial networks: from generating data to creating intelligence[J]. Acta Automatica Sinica, 2018, 44(5): 769-774.
[4] Krizhevsky A, Sutskever I, Hinton G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2017, 60(6): 84-90.
[5] Goodfellow IJ, Pouget-AbadieJ, MirzaM, et al. Generative adversarial nets[C]∥Proceedings of the 27th International Conference on Neural Information Processing Systems, December 8-13, 2014, Montreal, Quebec. New York: Curran Associates, 2014, 2: 2672- 2680.
[6] 李航. 统计学习方法[M]. 北京: 清华大学出版社, 2012: 13- 15.
LiH. Statistical learning method[M]. Beijing: Tsinghua University Press, 2012: 13- 15.
[7] Zhu JY, ParkT, IsolaP, 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. New York: IEEE, 2017: 2223- 2232.
[8] ChenX, DuanY, HouthooftR, et al. InfoGAN: interpretable representation learning by information maximizing generative adversarial nets[C]∥Proceedings of the 30th International Conference on Neural Information Processing Systems, December 5-10, 2016, Barcelona, Spain. New York: Curran Associates, 2016: 2180- 2188.
[9] Srivastava N, Hinton G, Krizhevsky A, et al. Dropout: a simple way to prevent neural networks from overfitting[J]. Journal of Machine Learning Research, 2014, 15: 1929-1958.
[10] GulrajaniI, AhmedF, ArjovskyM, et al. ( 2017-12-25)[2020-01-01]. https:∥arxiv.org/abs/1704. 00028.
[11] RadfordA, MetzL, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL]. ( 2016-01-07)[2020-01-01]. https:∥arxiv.org/abs/1511. 06434.
[12] GirshickR. Fast R-CNN[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 1440- 1448.
[13] ArjovskyM, ChintalaS, BottouL. Wasserstein generative adversarial networks[C]∥Proceedings of the 34th International Conference on Machine Learning, August 6-11, 2017, Sydney, NSW, Australia. New York: Curran Associates, 2017, 70: 214- 223.
[14] 王坤峰, 苟超, 段艳杰, 等. 生成式对抗网络GAN的研究进展与展望[J]. 自动化学报, 2017, 43(3): 321-332.
Wang K F, Gou C, Duan Y J, et al. Generative adversarial networks: the state of the art and beyond[J]. Acta Automatica Sinica, 2017, 43(3): 321-332.
[15] LongJ, ShelhamerE, DarrellT. Fully convolutional networks for semantic segmentation[C]∥2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 7-12, 2015, Boston, MA, USA. New York: IEEE, 2015: 3431- 3440.
[16] MiyatoT, KataokaT, KoyamaM, et al. ( 2018-02-16)[2020-01-01]. https:∥arxiv.org/abs/1802. 05957.
[17] He KM, Zhang XY, Ren SQ, 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. New York: IEEE, 2016: 770- 778.
[18] CheT, Li YR, Jacob AP, et al. ( 2017-03-02)[2020-01-01]. https:∥arxiv.org/abs/1612. 02136.
[19] He KM, Zhang XY, Ren SQ, et al. Delving deep into rectifiers: surpassing human-level performance on ImageNet classification[C]∥2015 IEEE International Conference on Computer Vision (ICCV), December 7-13, 2015, Santiago, Chile. New York: IEEE, 2015: 1026- 1034.
[20] YoshidaY, Miyato T. Spectral norm regularization for improving the generalizability of deep learning[EB/OL]. ( 2017-05-31)[2020-01-01]. https:∥arxiv.org/abs/1705. 10941.
[21] SalimansT, GoodfellowI, ZarembaW, et al. ( 2016-01-10)[2020-01-01]. https:∥arxiv.org/abs/1606.03498v1.
颜贝, 张礼, 张建林, 徐智勇. 基于残差结构的对抗式网络图像生成方法[J]. 激光与光电子学进展, 2020, 57(18): 181504. Bei Yan, Li Zhang, Jianlin Zhang, Zhiyong Xu. Image Generation Method for Adversarial Network Based on Residual Structure[J]. Laser & Optoelectronics Progress, 2020, 57(18): 181504.