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Goodfellow, J. Pouget-Abadie, M. Mirza, et al., Generative adversarial nets, Advances in neural information processing systems (2014), pp. 2672–2680.GoodfellowJ.Pouget-AbadieM.MirzaGenerative adversarial nets2014, pp.26722680Search in Google Scholar
M. Mirza, S. Osindero, Conditional Generative Adversarial Nets, Computer Science (2014) pp.2672–2680.M.MirzaS.OsinderoConditional Generative Adversarial Nets2014pp.26722680Search in Google Scholar
P. Isola, J.-Y. Zhu, T. Zhou, et al. Image-to-image translation with conditional adversarial networks, in the IEEE conference on computer vision and pattern recognition (CVPR) (2017), pp. 1125–1134.P.IsolaJ.-Y.ZhuT.Zhou, pp.11251134Search in Google Scholar
T. C. Wang, M. Y. Liu, J. Y. Zhu, A. Tao, J. Kautz, B. Catanzaro, High-resolution image synthesis and semantic manipulation with conditional GANs, In the IEEE conference on computer vision and pattern recognition (CVPR) (2018), pp. 8798–8807.T. C.WangM. Y.LiuJ. Y.ZhuA.TaoJ.KautzB.CatanzaroIn the IEEE conference on computer vision and pattern recognition (CVPR)2018, pp.87988807Search in Google Scholar
M. Zhai, L. Chen, F. Tung, J. He, M. Nawhal, G. Mori, Lifelong gan: Continual learning for conditional image generation. In the IEEE International Conference on Computer Vision(ICCV) (2019), pp. 2759–2768.M.ZhaiL.ChenF.TungJ.HeM.NawhalG.MoriIn the IEEE International Conference on Computer Vision(ICCV)2019, pp.27592768Search in Google Scholar
D. Bau, H. Strobelt, W. Peebles, et al. Semantic photo manipulation with a generative image prior[J]. arXiv preprint arXiv:2005.07727, 2020.D.BauH.StrobeltW.PeeblesarXiv preprint arXiv:2005.077272020Search in Google Scholar
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, Infogan: Interpretable representation learning by information maximizing generative adversarial nets, Advances in neural information processing systems (NIPS) (2016), pp. 2172–2180.X.ChenY.DuanR.HouthooftJ.SchulmanI.Sutskever, and P.AbbeelInfogan: Interpretable representation learning by information maximizing generative adversarial nets2016, pp.21722180Search in Google Scholar
J.Y. Zhu, R. Zhang, D. Pathak, T. Darrell, A. A. Efros, O. Wang, E. Shechtman, Toward multimodal image-to-image translation, Advances in neural information processing systems (NIPS) (2017), pp. 465–476.J.Y.ZhuR.ZhangD.PathakT.DarrellA. A.EfrosO.WangE.ShechtmanToward multimodal image-to-image translation2017, pp.465476Search in Google Scholar
J. Y. Zhu, T. Park, P. Isola, et al., Unpaired image-to-image translation using cycle-consistent adversarial networks, In the IEEE international conference on computer vision (ICCV) (2017), pp. 2223–2232.J. Y.ZhuT.ParkP.IsolaIn the IEEE international conference on computer vision (ICCV)2017, pp.22232232Search in Google Scholar
W. Xian, P. Sangkloy, V. Agrawal, et al. Texturegan: Controlling deep image synthesis with texture patches, In the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018), pp. 8456–8465.W.XianP.SangkloyV.AgrawalIn the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)2018, pp.84568465Search in Google Scholar
Y. Lu, S. Wu, Y. W. Tai, et al., Image generation from sketch constraint using contextual gan, in the European Conference on Computer Vision (ECCV) (2018), pp. 205–220.Y.LuS.WuY. W.Tai2018, pp.205220Search in Google Scholar
A. Gonzalez-Garcia, J. Van De Weijer, Y. Bengio, Image-to-image translation for cross-domain disentanglement, Advances in neural information processing systems (NIPS) (2018), pp. 1287–1298.A.Gonzalez-GarciaJ.Van De WeijerY.BengioImage-to-image translation for cross-domain disentanglement(2018), pp.12871298Search in Google Scholar
H. Tang, D. Xu, G. Liu, W. Wang, N. Sebe, Y. Yan, Cycle in cycle generative adversarial networks for keypoint-guided image generation. In the 27th ACM International Conference on Multimedia (2019, October), pp. 2052–2060.H.TangD.XuG.LiuW.WangN.SebeY.YanIn the 27th ACM International Conference on Multimedia(2019)October, pp.2052206010.1145/3343031.3350980Search in Google Scholar
Z. Gan, L. Chen, W. Wang, Y. Pu, Y. Zhang, H. Liu, C. Li, L. Carin, Triangle generative adversarial networks. In NIPS. (2017) pp. 5253–5262.Z.GanL.ChenW.WangY.PuY.ZhangH.LiuC.LiL.CarinTriangle generative adversarial networks(2017)pp.52535262Search in Google Scholar
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J. Stargan: Unified generative adversarial networks for multi-domain image-to-image translation. In CVPR. (2018)ChoiY.ChoiM.KimM.HaJ.W.KimS.ChooJ.Stargan: Unified generative adversarial networks for multi-domain image-to-image translation(2018)10.1109/CVPR.2018.00916Search in Google Scholar
Huang X, Liu M Y, Belongie S, et al. Multimodal unsupervised image-to-image translation. In ECCV. (2018)HuangXLiuM YBelongieSMultimodal unsupervised image-to-image translation(2018)10.1007/978-3-030-01219-9_11Search in Google Scholar
M.-Y. Liu, T. Breuel, and J. Kautz. Unsupervised image-to-image translation networks. In NIPS, 2017.M.-Y.LiuT.Breuel, and J.KautzUnsupervised image-to-image translation networks2017Search in Google Scholar
Taigman, Y., Polyak, A., Wolf, L. Unsupervised cross-domain image generation. In ICLR. (2017)TaigmanY.PolyakA.WolfL.Unsupervised cross-domain image generation(2017)Search in Google Scholar
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, D. Krishnan, Unsupervised pixel-level domain adaptation with generative adversarial networks. In CVPR. (2017)K.BousmalisN.SilbermanD.DohanD.ErhanD.KrishnanUnsupervised pixel-level domain adaptation with generative adversarial networks(2017)10.1109/CVPR.2017.18Search in Google Scholar
E. Hosseini-Asl, Y. Zhou, C. Xiong, R. Socher, Augmented cyclic adversarial learning for low resource domain adaptation (2018). arXiv preprint arXiv:1807.00374.E.Hosseini-AslY.ZhouC.XiongR.Socher(2018)arXiv preprint arXiv:1807.00374Search in Google Scholar
M. Y. Liu, X. Huang, A. Mallya, T. Karras, T. Aila, J. Lehtinen, J. Kautz, Few-shot unsupervised image-to-image translation, In the IEEE International Conference on Computer Vision (ICCV) (2019), pp. 10551–10560.M. Y.LiuX.HuangA.MallyaT.KarrasT.AilaJ.LehtinenJ.KautzIn the IEEE International Conference on Computer Vision (ICCV)(2019), pp.1055110560Search in Google Scholar
T. C. Wang, M. Y. Liu, A. Tao, G. Liu, J. Kautz, B. Catanzaro, Few-shot video-to-video synthesis, (2019) arXiv preprint arXiv:1910.12713.T. C.WangM. Y.LiuA.TaoG.LiuJ.KautzB.Catanzaro(2019)arXiv preprint arXiv:1910.12713Search in Google Scholar
A. Torralba, Contextual priming for object detection, International journal of computer vision, 2003, 53(2): 169–191.A.TorralbaContextual priming for object detection200353216919110.1023/A:1023052124951Search in Google Scholar
X. Wang and A. Gupta. Generative image modeling using style and structure adversarial networks. In ECCV. (2016)X.Wang and A.GuptaGenerative image modeling using style and structure adversarial networks(2016)10.1007/978-3-319-46493-0_20Search in Google Scholar
D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros. Context encoders: Feature learning by inpainting. In CVPR. (2016)D.PathakP.KrahenbuhlJ.DonahueT.Darrell, and A. A.EfrosContext encoders: Feature learning by inpainting(2016)10.1109/CVPR.2016.278Search in Google Scholar
D. Yoo, N. Kim, S. Park, A. S. Paek, and I. S. Kweon. Pixel-level domain transfer. In ECCV. (2016)D.YooN.KimS.ParkA. S.Paek, and I. S.KweonPixel-level domain transfer(2016)10.1007/978-3-319-46484-8_31Search in Google Scholar
K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition. In the IEEE con ference on computer vision and pattern recognition (CVPR) (2016), pp. 770–778.K.HeX.ZhangS.RenJ.SunDeep residual learning for image recognition(2016), pp.770778Search in Google Scholar