【每周CV论文】初学GAN图像风格化必须要读的文章

欢迎来到《每周CV论文》。在这个专栏里，还是本着有三AI一贯的原则，专注于让大家能够系统性完成学习，所以我们推荐的文章也必定是同一主题的。

图像风格化是生成对抗网络的一大应用，今天给大家推荐初学该领域必须要读的文章。

作者&编辑 | 言有三

[1] Isola P, Zhu J Y, Zhou T, et al. Image-to-image translation with conditional adversarial networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 1125-1134.

[2] Zhu J Y, Park T, Isola P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]//Proceedings of the IEEE international conference on computer vision. 2017: 2223-2232.

[3] Liu M, Breuel T M, Kautz J, et al. Unsupervised Image-to-Image Translation Networks[C]. neural information processing systems, 2017: 700-708.

[4] Choi Y, Choi M, Kim M, et al. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2018: 8789-8797.

[5] Choi Y, Uh Y, Yoo J, et al. StarGAN v2: Diverse Image Synthesis for Multiple Domains[J]. arXiv: Computer Vision and Pattern Recognition, 2019.

[6] Karras T, Laine S, Aila T. A style-based generator architecture for generative adversarial networks[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019: 4401-4410.

[7] Karras T, Laine S, Aittala M, et al. Analyzing and Improving the Image Quality of StyleGAN.[J]. arXiv: Computer Vision and Pattern Recognition, 2019.

[8] Li T, Qian R, Dong C, et al. BeautyGAN: Instance-level Facial Makeup Transfer with Deep Generative Adversarial Network[C]. acm multimedia, 2018: 645-653.

[9] Kim J, Kim M, Kang H, et al. U-GAT-IT: Unsupervised Generative Attentional Networks with Adaptive Layer-Instance Normalization for Image-to-Image Translation[C]. international conference on learning representations, 2020.