Conditional Image-to-Image Translation

Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Volumn , Issue , 2018, Pages 5524-5532

  Lin, Jianxin ^a   Xia, Yingce ^a   Qin, Tao ^b   Chen, Zhibo ^a   Liu, Tie Yan ^b  

^a UNIVERSITY OF SCIENCE AND TECHNOLOGY OF CHINA   (China)

^b MICROSOFT RESEARCH ASIA   (China)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SCIENCE; COMPUTERS; ELECTRICAL ENGINEERING; PATTERN RECOGNITION; SOFTWARE ENGINEERING;

ADVERSARIAL NETWORKS; B DOMAINS; DOMAIN INDEPENDENTS; DOMAIN SPECIFIC; IMAGE TRANSLATION; INPUT IMAGE; TARGET DOMAIN; TRANSLATION MODELS;

COMPUTER VISION;

EID: 85061803802     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2018.00579     Document Type: Conference Paper

References (25)

1. E. L. Denton, S. Chintala, R. Fergus, et al. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in neural information processing systems, pages 1486-1494, 2015.
2. J. Donahue, P. Krähenbühl, and T. Darrell. Adversarial feature learning. ArXiv preprint arXiv: 1605.09782, 2016.
3. V. Dumoulin, I. Belghazi, B. Poole, A. Lamb, M. Arjovsky, O. Mastropietro, and A. Courville. Adversarially learned inference. ArXiv preprint arXiv: 1606.00704, 2016.
4. L. A. Gatys, A. S. Ecker, and M. Bethge. Image style transfer using convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 2414-2423, 2016.
5. S. Goferman, L. Zelnik-Manor, and A. Tal. Context-Aware saliency detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 34(10): 1915-1926, 2012.
6. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D.Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Advances in neural information processing systems, pages 2672-2680, 2014.
7. D. He, Y. Xia, T. Qin, L.Wang, N. Yu, T. Liu, andW.-Y. Ma. Dual learning for machine translation. In Advances in Neural Information Processing Systems, pages 820-828, 2016.
8. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Imageto-image translation with conditional adversarial networks. ArXiv preprint arXiv: 1611.07004, 2016.
9. T. Kim, M. Cha, H. Kim, J. Lee, and J. Kim. Learning to discover cross-domain relations with generative adversarial networks. ArXiv preprint arXiv: 1703.05192, 2017.
10. D. Kingma and J. Ba. Adam: A method for stochastic optimization. ArXiv preprint arXiv: 1412.6980, 2014.
11. D. P. Kingma and M. Welling. Auto-encoding variational bayes. ArXiv preprint arXiv: 1312.6114, 2013.
12. Z. Liu, P. Luo, X. Wang, and X. Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), 2015.
13. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 3431-3440, 2015.
14. Y. Lu, Y.-W. Tai, and C.-K. Tang. Conditional cyclegan for attribute guided face image generation. ArXiv preprint arXiv: 1705.09966, 2017.
15. P. Luo, G. Wang, L. Lin, and X. Wang. Deep dual learning for semantic image segmentation. In The IEEE International Conference on Computer Vision (ICCV), Oct 2017.
16. A. L. Maas, A. Y. Hannun, and A. Y. Ng. Rectifier nonlinearities improve neural network acoustic models. In Proc. ICML, volume 30, 2013.
17. V. Nair and G. E. Hinton. Rectified linear units improve restricted boltzmann machines. In Proc. ICML, pages 807-814, 2010.
18. Y. Nesterov. A method of solving a convex programming problem with convergence rate o (1/k2). In Soviet Mathematics Doklady, volume 27, pages 372-376, 1983.
19. A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. ArXiv preprint arXiv: 1511.06434, 2015.
20. Y. Taigman, A. Polyak, and L. Wolf. Unsupervised crossdomain image generation. ArXiv preprint arXiv: 1611.02200, 2016.
21. Y. Xia, T. Qin, W. Chen, J. Bian, N. Yu, and T.-Y. Liu. Dual supervised learning. In D. Precup and Y. W. Teh, editors, Proceedings of the 34th International Conference on Machine Learning, volume 70 of Proceedings of Machine Learning Research, pages 3789-3798, International Convention Centre, Sydney, Australia, 06-11 Aug 2017. PMLR.
22. Z. Yi, H. Zhang, P. Tan, and M. Gong. Dualgan: Unsupervised dual learning for image-To-image translation. In The IEEE International Conference on Computer Vision (ICCV), Oct 2017.
23. A. Yu and K. Grauman. Fine-grained visual comparisons with local learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 192-199, 2014.
24. J.-Y. Zhu, P. Krähenbühl, E. Shechtman, and A. A. Efros. Generative visual manipulation on the natural image manifold. In European Conference on Computer Vision, pages 597-613. Springer, 2016.
25. J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros. Unpaired imageto-image translation using cycle-consistent adversarial networks. ArXiv preprint arXiv: 1703.10593, 2017.