[3] [3] SHORTEN C, KHOSHGOFTAAR T M.A survey on image data augmentation for deep learning［J］.Journal of Big Data, 2019, 6(1):60-72.

[5] [5] DENG J, DONG W, SOCHER R, et al.ImageNet:a large-scale hierarchical image database［C］//IEEE Conference on Computer Vision & Pattern Recognition (CVPR), 2009: 563-584.

[6] [6] HUANG S W, LIN C T, CHEN S P, et al.AugGAN:cross domain adaptation with GAN-based data augmentation［C］//European Conference on Computer Vision (ECCV), 2018:731-744.

[7] [7] DAWAR N, OSTADABBAS S, KEHTANAVAZ N.Data augmentation in deep learning-based fusion of depth and inertial sensing for action recognition［J］.IEEE Sensors Letters, 2019, 3(1):1-4.

[8] [8] ZHU J Y, ZHANG Z, ZHANG C, et al.Visual object networks:image generation with disentangled 3D representation［R］.Los Alamos: arXiv:1812.02725, 2018.

[9] [9] ZHU J Y, PARK T, ISOLA P, et al.Unpaired image-to-image translation using cycle-consistent adversarial networks［C］//IEEE International Conference on Computer Vision(ICCV), 2017:2342-2351.

[10] [10] SHAHAM T R, DEKEL T, MICHAELI T.SinGAN:learning a generative model from a single natural image［C］//International Conference on Computer Vision (ICCV), 2019:4569-4579.

[11] [11] CUBUK E D, ZOPH B, MAN D, et al.AutoAugment:learning augmentation strategies from data［C］//Conference on Computer Vision and Pattern Recognition(CV-PR), 2019:113-123.

[14] [14] LI C, WAN M.Precomputed real-time texture synthesis with Markovian generative adversarial networks［C］// European Conference on Computer Vision (ECCV), 2016:702-716.

[15] [15] GULRAJANII I, AHMED F, ARJOVSKY M, et al.Improved training of Wasserstein GAN ［R］.Los Alamos: arXiv:1704.00028, 2020.