Generating Sea Surface Object Image Using Image-to-Image Translation

[1] Goodfellow, J. Pouget-Abadie, M. Mirza, et al., Generative adversarial nets, Advances in neural information processing systems (2014), pp. 2672–2680. Goodfellow J. Pouget-Abadie M. Mirza Generative adversarial nets Advances in neural information processing systems 2014, pp. 2672 2680 Search in Google Scholar

[2] M. Mirza, S. Osindero, Conditional Generative Adversarial Nets, Computer Science (2014) pp.2672–2680. M. Mirza S. Osindero Conditional Generative Adversarial Nets Computer Science 2014 pp.2672 2680 Search in Google Scholar

[3] 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. Isola J.-Y. Zhu T. Zhou Image-to-image translation with conditional adversarial networks, in the IEEE conference on computer vision and pattern recognition (CVPR) (2017), pp. 1125 1134 Search in Google Scholar

[4] 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. 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 Search in Google Scholar

[5] 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. 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 Search in Google Scholar

[6] D. Bau, H. Strobelt, W. Peebles, et al. Semantic photo manipulation with a generative image prior[J]. arXiv preprint arXiv:2005.07727, 2020. D. Bau H. Strobelt W. Peebles Semantic photo manipulation with a generative image prior[J] arXiv preprint arXiv:2005.07727 2020 Search in Google Scholar

[7] 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. 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 Search in Google Scholar

[8] 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. 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 Search in Google Scholar

[9] 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. Zhu T. Park P. Isola Unpaired image-to-image translation using cycle-consistent adversarial networks In the IEEE international conference on computer vision (ICCV) 2017, pp. 2223 2232 Search in Google Scholar

[10] 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. Xian P. Sangkloy V. Agrawal Texturegan: Controlling deep image synthesis with texture patches In the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018, pp. 8456 8465 Search in Google Scholar

[11] 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. Lu S. Wu Y. W. Tai Image generation from sketch constraint using contextual gan, in the European Conference on Computer Vision (ECCV) 2018, pp. 205 220 Search in Google Scholar

[12] 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-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 Search in Google Scholar

[13] 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. 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 10.1145/3343031.3350980 Search in Google Scholar

[14] 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. 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 Search in Google Scholar

[15] 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) 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)10.1109/CVPR.2018.00916 Search in Google Scholar

[16] Huang X, Liu M Y, Belongie S, et al. Multimodal unsupervised image-to-image translation. In ECCV. (2018) Huang X Liu M Y Belongie S Multimodal unsupervised image-to-image translation In ECCV. (2018)10.1007/978-3-030-01219-9_11 Search in Google Scholar

[17] M.-Y. Liu, T. Breuel, and J. Kautz. Unsupervised image-to-image translation networks. In NIPS, 2017. M.-Y. Liu T. Breuel , and J. Kautz Unsupervised image-to-image translation networks In NIPS 2017 Search in Google Scholar

[18] Taigman, Y., Polyak, A., Wolf, L. Unsupervised cross-domain image generation. In ICLR. (2017) Taigman Y. Polyak A. Wolf L. Unsupervised cross-domain image generation In ICLR. (2017) Search in Google Scholar

[19] K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, D. Krishnan, Unsupervised pixel-level domain adaptation with generative adversarial networks. In CVPR. (2017) K. Bousmalis N. Silberman D. Dohan D. Erhan D. Krishnan Unsupervised pixel-level domain adaptation with generative adversarial networks In CVPR. (2017)10.1109/CVPR.2017.18 Search in Google Scholar

[20] 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-Asl Y. Zhou C. Xiong R. Socher Augmented cyclic adversarial learning for low resource domain adaptation (2018) arXiv preprint arXiv:1807.00374 Search in Google Scholar

[21] 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. 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 Search in Google Scholar

[22] 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. Wang M. Y. Liu A. Tao G. Liu J. Kautz B. Catanzaro Few-shot video-to-video synthesis, (2019) arXiv preprint arXiv:1910.12713 Search in Google Scholar

[23] A. Torralba, Contextual priming for object detection, International journal of computer vision, 2003, 53(2): 169–191. A. Torralba Contextual priming for object detection International journal of computer vision 2003 53 2 169 191 10.1023/A:1023052124951 Search in Google Scholar

[24] X. Wang and A. Gupta. Generative image modeling using style and structure adversarial networks. In ECCV. (2016) X. Wang and A. Gupta Generative image modeling using style and structure adversarial networks In ECCV. (2016)10.1007/978-3-319-46493-0_20 Search in Google Scholar

[25] D. Pathak, P. Krahenbuhl, J. Donahue, T. Darrell, and A. A. Efros. Context encoders: Feature learning by inpainting. In CVPR. (2016) D. Pathak P. Krahenbuhl J. Donahue T. Darrell , and A. A. Efros Context encoders: Feature learning by inpainting In CVPR. (2016)10.1109/CVPR.2016.278 Search in Google Scholar

[26] D. Yoo, N. Kim, S. Park, A. S. Paek, and I. S. Kweon. Pixel-level domain transfer. In ECCV. (2016) D. Yoo N. Kim S. Park A. S. Paek , and I. S. Kweon Pixel-level domain transfer In ECCV. (2016)10.1007/978-3-319-46484-8_31 Search in Google Scholar

[27] 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. 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 Search in Google Scholar