[1] |
谭明奎, 许守恺, 张书海, 等. 深度对抗视觉生成综述[J]. 中国图象图形学报, 2021, 26(12):2751-2766.
|
|
Tan Mingkui, Xu Shoukai, Zhang Shuhai, et al. A review on deep adversarial visual generation[J]. Journal of Image and Graphics, 2021, 26(12):2751-2766.
|
[2] |
佟博, 刘韬, 刘畅. 基于生成对抗网络的轴承失效信号生成研究[J]. 电子科技, 2020, 33(4):28-34.
|
|
Tong Bo, Liu Tao, Liu Chang. Research on bearing failure signal generation based on generative adversarial networks[J]. Electronic Science and Technology, 2020, 33(4):28-34.
|
[3] |
Goodfellow I J, Pouget-Abadie J, Mirza M, et al. Generative adversarial networks[J]. Communications of the ACM, 2020, 63(11):139-144.
doi: 10.1145/3422622
|
[4] |
Radford A, Metz L, Chintala S. Unsupervised representation learning with deep convolutional generative adversarial networks[C].San Diego: Proceedings of the Fourth International Conference on Learning Representations, 2016:1511-1526.
|
[5] |
Arjovsky M, Bottou L. Towards principled methods for training generative adversarial networks[C].Toulon: Proceedings of the Fifth International Conference on Learning Representations, 2017:1701-1717.
|
[6] |
Karras T, Aila T, Laine S, et al. Progressive growing of gans for improved quality, stability and variation[C]. Vancouver: Proceedings of the Sixth International Conference on Learning Representations, 2018:1710-1735.
|
[7] |
Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarical networks[C].Vancouver: Proceedings of the Sixth International Conference on Learning Representations, 2018:2242-2251.
|
[8] |
Zhang H, Goodfellow I, Metaxas D, et al. Self-attention generative adversarial networks[C].Long Beach: International Conference on Machine Learning, 2019:7354-7363.
|
[9] |
Brock A, Donahue J, Simonyan K. Large scale GAN training for high fidelity natural image synthesis[C].New Orleans: Proceedings of the Seventh International Conference on Learning Representations, 2019:1809-1843.
|
[10] |
Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks[J]. IEEE Computer Society, 2021, 43(12):4217-4228
|
[11] |
Song T, Jia W. Alleviation of gradient exploding in GANs: Fake can be real[C].Seattle: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:1191-1200.
|
[12] |
史彩娟, 涂冬景, 刘靖祎. Re-GAN:残差生成式对抗网络算法[J]. 中国图象图形学报, 2021, 26(3):594-604.
|
|
Shi Caijuan, Tu Dongjing, Liu Jingyi. Re-GAN: Residual generative adversarial networks algorithm[J]. Journal of Image and Graphics, 2021, 26(3):594-604.
|
[13] |
Karnewar A, Wang O. MSG-GAN: Multi-scale gradients for generative adversarial networks[C].Seattle: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:7796-7805.
|
[14] |
Li Z, Yang J, Liu Z, et al. Feedback network for image super-resolution[C].Seoul: Proceedings of the Thirty-Second IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019:3867-3876.
|
[15] |
Chen Y, Dai X, Liu M, et al. Dynamic convolution: Attention over convolution kernels[C].Seattle: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:11027-11036.
|
[16] |
Wang Y, Chen Y C, Zhang X, et al. Attentive normalization for conditional image generation[C].Seattle: IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2020:5093-5102.
|
[17] |
张哲新, 原俊青, 郭欢磊, 等. 多判别器协同框架:高品质图像的谱归一生成对抗网络[J]. 小型微型计算机系统, 2021, 42(1):201-207.
|
|
Zhang Zhexin, Yuan Junqing, Guo Huanlei, et al. Multi-discriminator co-operation framework: Spectral normalized generative adversarial networks for high quality generated images[J]. Journal of Chinese Computer Systems, 2021, 42(1):201-207.
|