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There are many recent research efforts to fine-tune a pre-trained generator with a few target images to generate images of a novel domain.
X. Wang and X. Tang, “Face photo-sketch synthesis and recognition,” IEEE transactions on pattern analysis and machine intelligence , vol. 31, no. 11, pp. 1955–1967, 2008
2008
Earlier work this paper cites.
I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” ser. NIPS’14. Cambridge, MA, USA: MIT Press, 2014, p. 2672–2680
2014
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2014
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2015
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J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired image-to-image translation using cycle-consistent adversarial networks,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , Oct 2017
2017
Earlier work this paper cites.
S. Benaim and L. Wolf, “One-sided unsupervised domain mapping,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30. Curran Associates, Inc., 2017. [Online]. Available: https://proceedings.neurips.cc/paper/2017/file/59b90e1005a220e2ebc542eb9d950b1e-Paper.pdf
2017
Earlier work this paper cites.
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter, “Gans trained by a two time-scale update rule converge to a local nash equilibrium,” 2017, pp. 6626–6637
2017
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Y. Wang, C. Wu, L. Herranz, J. van de Weijer, A. Gonzalez-Garcia, and B. Raducanu, “Transferring gans: generating images from limited data,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 218–234
2018
Earlier work this paper cites.
T. Karras, T. Aila, S. Laine, and J. Lehtinen, “Progressive growing of GANs for improved quality, stability, and variation,” 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Y. Choi, M. Choi, M. Kim, J.-W. Ha, S. Kim, and J. Choo, “Stargan: Unified generative adversarial networks for multi-domain image-to-image translation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 8789–8797
2018
Earlier work this paper cites.
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang, “The unreasonable effectiveness of deep features as a perceptual metric,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 586–595
2018
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A. Noguchi and T. Harada, “Image generation from small datasets via batch statistics adaptation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2750–2758
2019
Earlier work this paper cites.
T. Karras, S. Laine, and T. Aila, “A style-based generator architecture for generative adversarial networks,” 2019, pp. 4401–4410
2019
Earlier work this paper cites.
H. Fu, M. Gong, C. Wang, K. Batmanghelich, K. Zhang, and D. Tao, “Geometry-consistent generative adversarial networks for one-sided unsupervised domain mapping,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2019
2019
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R. Abdal, Y. Qin, and P. Wonka, “Image2stylegan: How to embed images into the stylegan latent space?” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 4432–4441
2019
Earlier work this paper cites.
J. Deng, J. Guo, N. Xue, and S. Zafeiriou, “Arcface: Additive angular margin loss for deep face recognition,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 4690–4699
2019
Earlier work this paper cites.
D. Y. Park and K. H. Lee, “Arbitrary style transfer with style-attentional networks,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2019, pp. 5880–5888
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
Y. Alharbi and P. Wonka, “Disentangled image generation through structured noise injection,” 2020, pp. 5134–5142
2020
Cited alongside, same era.
S. Pidhorskyi, D. A. Adjeroh, and G. Doretto, “Adversarial latent autoencoders,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 14 104–14 113
2020
Cited alongside, same era.
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila, “Analyzing and improving the image quality of stylegan,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 8110–8119
2020
Cited alongside, same era.
G. Kwon and J. C. Ye, “Diagonal attention and style-based gan for content-style disentanglement in image generation and translation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 13 980–13 989
2021
Later among the works it cites.
H. Kim, Y. Choi, J. Kim, S. Yoo, and Y. Uh, “Exploiting spatial dimensions of latent in gan for real-time image editing,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 852–861
2021
Later among the works it cites.
Y. Zhang, H. Ling, J. Gao, K. Yin, J.-F. Lafleche, A. Barriuso, A. Torralba, and S. Fidler, “Datasetgan: Efficient labeled data factory with minimal human effort,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 10 145–10 155
2021
Later among the works it cites.
R. Abdal, P. Zhu, N. J. Mitra, and P. Wonka, “Labels4free: Unsupervised segmentation using stylegan,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 13 970–13 979
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T. Karras, M. Aittala, J. Hellsten, S. Laine, J. Lehtinen, and T. Aila, “Training generative adversarial networks with limited data,” Advances in Neural Information Processing Systems , vol. 33, pp. 12 104–12 114, 2020
2020
Cited alongside, same era.
A. Tewari, M. Elgharib, G. Bharaj, F. Bernard, H.-P. Seidel, P. Pérez, M. Zollhofer, and C. Theobalt, “Stylerig: Rigging stylegan for 3d control over portrait images,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 6142–6151
2020
Cited alongside, same era.
S. Menon, A. Damian, S. Hu, N. Ravi, and C. Rudin, “Pulse: Self-supervised photo upsampling via latent space exploration of generative models,” in Proceedings of the ieee/cvf conference on computer vision and pattern recognition , 2020, pp. 2437–2445
2020
Cited alongside, same era.
T. Park, A. A. Efros, R. Zhang, and J.-Y. Zhu, “Contrastive learning for unpaired image-to-image translation,” in Computer Vision – ECCV 2020 , A. Vedaldi, H. Bischof, T. Brox, and J.-M. Frahm, Eds. Cham: Springer International Publishing, 2020, pp. 319–345
2020
Cited alongside, same era.
Y. Choi, Y. Uh, J. Yoo, and J.-W. Ha, “StarGAN v2: Diverse image synthesis for multiple domains,” 2020, pp. 8188–8197
2020
Cited alongside, same era.
E. Härkönen, A. Hertzmann, J. Lehtinen, and S. Paris, “Ganspace: Discovering interpretable gan controls,” in Advances in Neural Information Processing Systems , H. Larochelle, M. Ranzato, R. Hadsell, M. F. Balcan, and H. Lin, Eds., vol. 33. Curran Associates, Inc., 2020, pp. 9841–9850. [Online]. Available: https://proceedings.neurips.cc/paper/2020/file/6fe43269967adbb64ec6149852b5cc3e-Paper.pdf
2020
Cited alongside, same era.
Y. Shen, J. Gu, X. Tang, and B. Zhou, “Interpreting the latent space of gans for semantic face editing,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) . Los Alamitos, CA, USA: IEEE Computer Society, jun 2020, pp. 9240–9249. [Online]. Available: https://doi.ieeecomputersociety.org/10.1109/CVPR42600.2020.00926
2020
Cited alongside, same era.
2020
Cited alongside, same era.
2021
Later among the works it cites.
Y. Shi, D. Aggarwal, and A. K. Jain, “Lifting 2d stylegan for 3d-aware face generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 6258–6266
2021
Later among the works it cites.
T. Yang, P. Ren, X. Xie, and L. Zhang, “Gan prior embedded network for blind face restoration in the wild,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 672–681
2021
Later among the works it cites.
2021
Later among the works it cites.
O. Tov, Y. Alaluf, Y. Nitzan, O. Patashnik, and D. Cohen-Or, “Designing an encoder for stylegan image manipulation,” ACM Transactions on Graphics (TOG) , vol. 40, no. 4, pp. 1–14, 2021
2021
Later among the works it cites.
E. Richardson, Y. Alaluf, O. Patashnik, Y. Nitzan, Y. Azar, S. Shapiro, and D. Cohen-Or, “Encoding in style: A stylegan encoder for image-to-image translation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2021, pp. 2287–2296
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
M. Afifi, M. A. Brubaker, and M. S. Brown, “Histogan: Controlling colors of gan-generated and real images via color histograms,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2021
2021
Later among the works it cites.
S. Liu, T. Lin, D. He, F. Li, M. Wang, X. Li, Z. Sun, Q. Li, and E. Ding, “Adaattn: Revisit attention mechanism in arbitrary neural style transfer,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 6649–6658
2021
Later among the works it cites.
J. Xiao, L. Li, C. Wang, Z.-J. Zha, and Q. Huang, “Few shot generative model adaption via relaxed spatial structural alignment,” in 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022, pp. 11 194–11 203
2022
Closest in time.
T. Kramberger and B. Potočnik, “Lsun-stanford car dataset: Enhancing large-scale car image datasets using deep learning for usage in gan training,” Applied Sciences , vol. 10, no. 14, jul 2020. [Online]. Available: https://www.mdpi.com/2076-3417/10/14/4913
2076
Closest in time.
O. Patashnik, Z. Wu, E. Shechtman, D. Cohen-Or, and D. Lischinski, “Styleclip: Text-driven manipulation of stylegan imagery,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 2085–2094
2094
Closest in time.