Least Squares Generative Adversarial Networks

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 2813-2821

  Mao, Xudong ^a   Li, Qing ^a   Xie, Haoran ^b   Lau, Raymond Y K ^a   Wang, Zhen ^c   Smolley, Stephen Paul ^d  

^a CITY UNIVERSITY OF HONG KONG   (Hong Kong)

^b THE EDUCATION UNIVERSITY OF HONG KONG   (Hong Kong)

^c NORTHWESTERN POLYTECHNICAL UNIVERSITY   (China)

^d CodeHatch Corp ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS;

ADVERSARIAL NETWORKS; CROSS ENTROPY; LEARNING PROCESS; LEAST SQUARE; LOSS FUNCTIONS; OBJECTIVE FUNCTIONS; QUALITY IMAGE; VANISHING GRADIENT;

COMPUTER VISION;

EID: 85041908569     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.304     Document Type: Conference Paper

References (33)

1. M. Abadi, A. Agarwal, P. Barham, and et al. Tensor-Flow: Large-scale machine learning on heterogeneous systems, 2015.
2. M. Arjovsky, S. Chintala, and L. Bottou. Wasserstein gan. arXiv:1701. 07875, 2017.
3. T. Che, Y. Li, A. P. Jacob, Y. Bengio, and W. Li. Mode regularized generative adversarial networks. arXiv:1612. 02136, 2016.
4. X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel. Infogan: Interpretable representation learning by information maximizing generative adversarial nets. In Advances in Neural Information Processing Systems (NIPS), pages 2172-2180, 2016.
5. E. Denton, S. Chintala, A. Szlam, and R. Fergus. Deep generative image models using a laplacian pyramid of adversarial networks. In Advances in Neural Information Processing Systems (NIPS), pages 1486-1494, 2015.
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 (NIPS), pages 2672-2680, 2014.
7. K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In Computer Vision and Pattern Recognition (CVPR), 2016.
8. G. Hinton and R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313 (5786):504-507, 2006.
9. K. Hornik, M. Stinchcombe, and H. White. Multilayer feedforward networks are universal approximators. Neural Networks, 2 (5):359-366, July 1989.
10. X. Huang, Y. Li, O. Poursaeed, J. Hopcroft, and S. Belongie. Stacked generative adversarial networks. arXiv:1612. 04357, 2016.
11. S. Ioffe and C. Szegedy. Generative adversarial text-to-image synthesis. In Proceedings of The 33rd International Conference on Machine Learning (ICML), 2015.
12. P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros. Imageto-image translation with conditional adversarial networks. arXiv:1611. 07004, 2016.
13. D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv:1412. 6980, 2014.
14. D. P. Kingma and M. Welling. Auto-encoding variational bayes. In International Conference on Learning Representations (ICLR), 2014.
15. A. Krizhevsky. Learning multiple layers of features from tiny images. Tech Report, 2009.
16. C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, and W. Shi. Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network. arXiv:1609. 04802, 2016.
17. C.-L. Liu, F. Yin, Q.-F. Wang, and D.-H. Wang. Icdar 2011 chinese handwriting recognition competition. In Proceedings of the 2011 International Conference on Document Analysis and Recognition (ICDAR), pages 1464-1469, 2011.
18. J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. In Computer Vision and Pattern Recognition (CVPR), 2015.
19. L. Metz, B. Poole, D. Pfau, and J. Sohl-Dickstein. Unrolled generative adversarial networks. arXiv:1611. 02163, 2016.
20. M. Mirza and S. Osindero. Conditional Generative Adversarial Nets. arXiv:1411. 1784, 2014.
21. A. Nguyen, J. Yosinski, Y. Bengio, A. Dosovitskiy, and J. Clune. Plug & play generative networks: Conditional iterative generation of images in latent space. arXiv:1612. 00005, 2016.
22. X. Nguyen, M. J. Wainwright, and M. I. Jordan. Estimating divergence functionals and the likelihood ratio by convex risk minimization. IEEE Transactions on Information Theory, 56 (11):5847-5861, 2010.
23. S. Nowozin, B. Cseke, and R. Tomioka. f-gan: Training generative neural samplers using variational divergence minimization. arXiv:1606. 00709, 2016.
24. G.-J. Qi. Loss-sensitive generative adversarial networks on lipschitz densities. arXiv:1701. 06264, 2017.
25. A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In International Conference on Learning Representations (ICLR), 2015.
26. S. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele, and H. Lee. Generative adversarial text-to-image synthesis. In Proceedings of The 33rd International Conference on Machine Learning (ICML), 2016.
27. S. Ren, K. He, R. Girshick, and J. Sun. Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in Neural Information Processing Systems 28, pages 91-99, 2015.
28. R. Salakhutdinov and G. Hinton. Deep Boltzmann machines. In Proceedings of the International Conference on Artificial Intelligence and Statistics, volume 5, pages 448-455, 2009.
29. T. Salimans, I. Goodfellow, W. Zaremba, V. Cheung, A. Radford, X. Chen, and X. Chen. Improved techniques for training gans. In Advances in Neural Information Processing Systems (NIPS), pages 2226-2234, 2016.
30. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations (ICLR), 2015.
31. T. Tieleman and G. Hinton. Lecture 6. 5-RMSProp: Divide the gradient by a running average of its recent magnitude. COURSERA: Neural Networks for Machine Learning, 2012.
32. 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:1506. 03365, 2015.
33. J. Zhao, M. Mathieu, and Y. LeCun. Energy-based Generative Adversarial Network. arXiv:1609. 03126, 2016.