[1] [1] Schlegl T, Seebck P, Waldstein S M, et al. F-AnoGAN: fast unsupervised anomaly detection with generative adversarial networks[J]. Medical Image Analysis, 2019, 54: 30-44.

[2] [2] Lin C H, Yumer E, Wang O, et al. ST-GAN: spatial transformer generative adversarial networks for image compositing[C]//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018: 9455-9464.

[3] [3] 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, 2017: 2242-2251.

[4] [4] Karras T, Laine S, Aittala M, et al. Analyzing and improving the image quality of StyleGAN[C]//2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020: 8107-8116.

[5] [5] Wang Y, Wu C, Herranz L, et al. Transferring GANs: generating images from limited data[C]//Computer Vision –ECCV 2018, 2018: 220-236.

[6] [6] Antoniou A, Storkey A, Edwards H. Data augmentation generative adversarial networks[J]. arXiv preprint arXiv: 1711.04340, 2017.

[7] [7] Shrivastava A, Pfister T, Tuzel O, et al. Learning from simulated and unsupervised images through adversarial training[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition, 2017: 2242-2251.

[8] [8] Zhang Y, Ling H, Gao J, et al. DatasetGAN: efficient labeled data factory with minimal human effort[C]//2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021: 10140-10150.

[9] [9] Roy H, Chaudhury S, Yamasaki T, et al. Image inpainting using frequency-domain priors[J]. Journal of Electronic Imaging, 2021, 30(2): 023016.

[11] [11] Johnson J, Alahi A, Li F F. Perceptual losses for real-time style transfer and super-resolution[C]//Computer Vision–ECCV 2016, 2016: 694-711.

[13] [13] Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks[C]//2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019: 4396-4405.

[14] [14] Kaselimi M, Voulodimos A, Protopapadakis E, et al. EnerGAN: a generative adversarial network for energy disaggregation[C]//ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing, 2020: 1578-1582.

[15] [15] Mehralian M, Karasfi B. RDCGAN: unsupervised representation learning with regularized deep convolutional generative adversarial networks[C]//2018 9th Conference on Artificial Intelligence and Robotics and 2nd Asia-Pacific International Symposium, 2018: 31-38.

[16] [16] Mao X, Li Q, Xie H, et al. Least squares generative adversarial networks[C]//2017 IEEE International Conference on Computer Vision, 2017: 2813-2821.

[17] [17] Gulrajani I, Ahmed F, Arjovsky M, et al. Improved training of wasserstein gans[C]//NIPS'17: Proceedings of the 31st International Conference on Neural Information Processing Systems, 2017: 5769-5779.

[18] [18] Liu B, Zhu Y, Song K, et al. Towards faster and stabilized GAN training for high-fidelity few-shot image synthesis[C]//International Conference on Learning Representations, 2021: 1-13.