Unsupervised Creation of Parameterized Avatars

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 1539-1547

  Wolf, Lior ^a,b   Taigman, Yaniv ^a   Polyak, Adam ^a  

^a FACEBOOK AI RESEARCH   (United States)

^b TEL AVIV UNIVERSITY   (Israel)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; MAPPING;

CROSS-DOMAIN; DOMAIN ADAPTATION; GENERALIZATION BOUND; INPUT IMAGE; LEARNING PROBLEM; NETWORK SOLUTIONS; PARAMETERIZED;

PROBLEM SOLVING;

EID: 85041924399     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.170     Document Type: Conference Paper

References (28)

1. S. Ben-David, J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. W. Vaughan. A theory of learning from different domains. Machine Learning, 79(1-2):151-175, 2010
2. K. Crammer, M. Kearns, and J. Wortman. Learning from multiple sources. J. Mach. Learn. Res., 9:1757-1774, June 2008
3. A. Dosovitskiy and T. Brox. Generating images with perceptual similarity metrics based on deep networks. ArXiv preprint arXiv:1602.02644, 2016
4. A. Dosovitskiy, J. T. Springenberg, and T. Brox. Learning to generate chairs with convolutional neural networks. In CVPR, pages 1538-1546, 2015
5. T. Galanti and L.Wolf. A theory of output-side unsupervised domain adaptation. ArXiv preprint arXiv:1703.01606, 2017
6. Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky. Domainadversarial training of neural networks. J. Mach. Learn. Res., 17(1):2096-2030, 2016
7. L. A. Gatys, A. S. Ecker, and M. Bethge. Image style transfer using convolutional neural networks. In CVPR, 2016
8. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D.Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In NIPS, pages 2672-2680. 2014
9. K. Gregor, I. Danihelka, A. Graves, D. Rezende, and D. Wierstra. Draw: A recurrent neural network for image generation. In ICML, pages 1462-1471, 2015
10. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image-to-image translation with conditional adversarial networks. In CVPR, 2017
11. D. Jimenez Rezende, S. M. A. Eslami, S. Mohamed, P. Battaglia, M. Jaderberg, and N. Heess. Unsupervised learning of 3d structure from images. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, pages 4996-5004. Curran Associates, Inc., 2016
12. J. Johnson, A. Alahi, and L. Fei-Fei. Perceptual losses for real-time style transfer and super-resolution. In ECCV, 2016
13. T. D. Kulkarni, W. F. Whitney, P. Kohli, and J. Tenenbaum. Deep convolutional inverse graphics network. In NIPS, pages 2539-2547. 2015
14. M. Lin, Q. Chen, and S. Yan. Network In Network. In ICLR, 2014
15. M.-Y. Liu and O. Tuzel. Coupled generative adversarial networks. In NIPS, pages 469-477. 2016
16. 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
17. Y. Mansour, M. Mohri, and A. Rostamizadeh. Domain adaptation: Learning bounds and algorithms. In COLT-The 22nd Conference on Learning Theory, 2009
18. M. Mirza and S. Osindero. Conditional generative adversarial nets. ArXiv preprint arXiv:1411.1784, 2014
19. H. Ng and S. Winkler. A data-driven approach to cleaning large face datasets. In ICIP, 2014
20. O. M. Parkhi, A. Vedaldi, and A. Zisserman. Deep face recognition. In British Machine Vision Conference, 2015
21. A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. ArXiv preprint arXiv:1511.06434, 2015
22. S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee. Generative adversarial text to image synthesis. In ICML, 2016
23. Y. Taigman, A. Polyak, and L. Wolf. Unsupervised crossdomain image generation. In International Conference on Learning Representations (ICLR), 2017
24. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In CVPR, 2014
25. D. Ulyanov, V. Lebedev, A. Vedaldi, and V. Lempitsky. Texture networks: Feed-forward synthesis of textures and stylized images. In ICML, 2016
26. N. Wang, D. Tao, X. Gao, X. Li, and J. Li. Transductive face sketch-photo synthesis. IEEE transactions on neural networks and learning systems, 24(9):1364-1376, 2013
27. R. J. Williams. Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8:229-256, 1992
28. Y. Zhang, N. Wang, S. Zhang, J. Li, and X. Gao. Fast face sketch synthesis via kd-tree search. In G. Hua and H. Jégou, editors, ECCV, 2016.