Sphere generative adversarial network based on geometric moment matching

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

Volumn 2019-June, Issue , 2019, Pages 4287-4296

  Park, Sung Woo ^a   Kwon, Junseok ^a  

^a Chung Ang University   (South Korea)

Author keywords

Deep Learning; Image and Video Synthesis

Indexed keywords

COMPUTER VISION; DEEP LEARNING; HIGHER ORDER STATISTICS;

ADVERSARIAL NETWORKS; GEOMETRIC MOMENT; HYPERSPHERE; IMAGE GENERATIONS; OBJECTIVE FUNCTIONS; RECENT STATE; SOURCE CODES; VIDEO SYNTHESIS;

SPHERES;

EID: 85078775815     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2019.00442     Document Type: Conference Paper

References (41)

1. P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. Guibas. Learning representations and generative models for 3D point clouds. In ICLR, 2018.
2. M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein generative adversarial networks. In ICML, 2017.
3. M. G. Bellemare, I. Danihelka, W. Dabney, S. Mohamed, B. Lakshminarayanan, S. Hoyer, and R. Munos. The cramer distance as a solution to biased wasserstein gradients. arXiv preprint arXiv: 1705.10743, 2017.
4. M. Bikowski, D. J. Sutherland, M. Arbel, and A. Gretton. Demystifying MMD GANs. In ICLR, 2018.
5. Y. Chen, Y.-K. Lai, and Y.-J. Liu. Cartoongan: Generative adversarial networks for photo cartoonization. In CVPR, 2018.
6. A. Coates, A. Ng, and H. Lee. An analysis of single-layer networks in unsupervised feature learning. In AISTATS, 2011.
7. K. Ehsani, R. Mottaghi, and A. Farhadi. Segan: Segmenting and generating the invisible. In CVPR, 2018.
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, 2014.
9. I. Gulrajani, F. Ahmed, M. Arjovsky, V. Dumoulin, and A. C. Courville. Improved training of wasserstein GANs. In NIPS, 2017.
10. P. Henderson, W.-D. Chang, P.-L. Bacon, D. Meger, J. Pineau, and D. Precup. Optiongan: Learning joint rewardpolicy options using generative adversarial inverse reinforcement learning. In AAAI, 2018.
11. 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. In NIPS, 2017.
12. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Image-To-image translation with conditional adversarial nets. In CVPR, 2017.
13. A. Krizhevsky. Learning multiple layers of features from tiny images. Technical report, Citeseer, 2009.
14. C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. P. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi. Photo-realistic single image super-resolution using a generative adversarial network. In CVPR, 2017.
15. J. Lei Ba, J. R. Kiros, and G. E. Hinton. Layer normalization. arXiv preprint arXiv: 1607.06450, 2016.
16. C.-L. Li, W.-C. Chang, Y. Cheng, Y. Yang, and B. Poczos. Mmd gan: Towards deeper understanding of moment matching network. In NIPS, 2017.
17. J. Lin, Y. Xia, T. Qin, Z. Chen, and T.-Y. Liu. Conditional image-To-image translation. In CVPR, 2018.
18. X. Mao, Q. Li, H. Xie, R. Y. Lau, Z. Wang, and S. P. Smolley. Least squares generative adversarial networks. arXiv preprint arXiv: 1611.04076, 2017.
19. M. Mathieu, C. Couprie, and Y. LeCun. Deep multi-scale video prediction beyond mean square error. In ICLR, 2016.
20. T. Miyato, T. Kataoka, M. Koyama, and Y. Yoshida. Spectral normalization for generative adversarial networks. In ICLR, 2018.
21. Y. Mroueh, C.-L. Li, T. Sercu, A. Raj, and Y. Cheng. Sobolev GAN. In ICLR, 2018.
22. Y. Mroueh and T. Sercu. Fisher gan. In NIPS, 2017.
23. A. Müller. Integral probability metrics and their generating classes of functions. Advances in Applied Probability, 29(2): 429-443, 1997.
24. H. Petzka, A. Fischer, and D. Lukovnikov. On the regularization of wasserstein GANs. In ICLR, 2018.
25. A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv: 1511.06434, 2015.
26. S. Reed, Z. Akata, L. X. Yan, B. Logeswaran, Schiele, and H. Lee. Generative adversarial text to image synthesis. In ICML, 2016.
27. T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, and X. Chen. Improved techniques for training GANs. In NIPS, 2016.
28. Y. Song, C. Ma, X. Wu, L. Gong, L. Bao, W. Zuo, C. Shen, R. Lau, and M.-H. Yang. Vital: Visual tracking via adversarial learning. In CVPR, 2018.
29. C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. Rethinking the inception architecture for computer vision. In CVPR, 2016.
30. G. J. Székely and M. L. Rizzo. Energy statistics: A class of statistics based on distances. Journal of statistical planning and inference, 143(8): 1249-1272, 2013.
31. C. Tao, L. Chen, R. Henao, J. Feng, and L. C. Duke. Chisquare generative adversarial network. In ICML, 2018.
32. T. Unterthiner, B. Nessler, C. Seward, G. Klambauer, M. Heusel, H. Ramsauer, and S. Hochreiter. Coulomb GANs: Provably optimal nash equilibria via potential fields. In ICLR, 2018.
33. C. Villani. Optimal Transport: Old and New. Springer Berlin Heidelberg, 2008.
34. X. Wang and A. Gupta. Generative image modeling using style and structure adversarial networks. In ECCV, 2016.
35. X. Wang, A. Shrivastava, and A. Gupta. A-fast-rcnn: Hard positive generation via adversary for object detection. In CVPR, 2017.
36. D. Warde-Farley and Y. Bengio. Improving generative adversarial networks with denoising feature matching. In ICLR, 2017.
37. X. Wei, Z. Liu, L. Wang, and B. Gong. Improving the improved training of wasserstein GANs. In ICLR, 2018.
38. F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao. Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv: 1506.03365, 2015.
39. J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang. Generative image inpainting with contextual attention. In CVPR, 2018.
40. Z. Zhang, L. Yang, and Y. Zheng. Translating and segmenting multimodal medical volumes with cycle-And shapeconsistency generative adversarial network. In CVPR, 2018.
41. J. Zhao, M. Mathieu, and Y. LeCun. Energy-based generative adversarial network. In ICLR, 2017.