Robust Boltzmann Machines for recognition and denoising

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

Volumn , Issue , 2012, Pages 2264-2271

  Tang, Yichuan ^a   Salakhutdinov, Ruslan ^a   Hinton, Geoffrey ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

BOLTZMANN MACHINES; DE-NOISING; DENSITY MODELING; FACE DATABASE; NOISY DATA; SCALE MIXTURES; SPATIAL STRUCTURE; SPEECH DATA; STANDARD ALGORITHMS; VISUAL RECOGNITION;

FACE RECOGNITION;

COMPUTER VISION;

EID: 84866720201     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2012.6247936     Document Type: Conference Paper

References (29)

1. The yale face database., 2006. http://cvc.yale.edu/projects/yalefaces/ yalefaces.html. 4
2. S. Geman and D. Geman. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images. PAMI, PAMI-6(6):721-741, 1984. 3
3. N. Heess, N. L. Roux, and J.Winn. Weakly supervised learning of foreground-background segmentation using masked RBMs. In International Conference on Artificial Neural Networks (2011), July 19 2011. 1
4. G. E. Hinton and R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313:504-507, 2006. 2
5. P. J. Huber. Robust Statistics. Wiley series in probability and mathematical statistics. John Wiley & Sons, Inc., 1981. 3
6. H. J. Jia and A. M. Martinez. Face recognition with occlusions in the training and testing sets. In IEEE Int. Conf. on Automatic Face and Gesture Recognition, 2008. 1
7. A. Kannan, N. Jojic, and B. J. Frey. Generative model for layers of appearance and deformation. In AISTATS, 2005, 2005. 1
8. A. Krizhevsky. Learning multiple layers of features from tiny images, 2009. 2
9. Y. LeCun, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, Nov. 1998. 1
10. H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Intl. Conf. on Machine Learning, pages 609-616, 2009. 2
11. D. G. Lowe. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 60:91-110, 2004. 1
12. J. Lu, K. N. Plataniotis, and A. N. Venetsanopoulos. Face recognition using kernel direct discriminant analysis algorithms. IEEE-NN, 14:117-126, Jan. 2003. 6, 7
13. A. Martinez and R. Benavente. The ar face database, Jun 1998. 4
14. A. Mohamed, G. Dahl, and G. Hinton. Acoustic modeling using deep belief networks. IEEE Transactions on Audio, Speech, and Language Processing, 2011. 1, 2
15. M. Ranzato, J. Susskind, V. Mnih, and G. Hinton. On Deep Generative Models with Applications to Recognition. In CVPR, 2011. 1
16. S. Roth and M. J. Black. Fields of experts: A framework for learning image priors. In IEEE Conf. on Computer Vision and Pattern Recognition, pages 860-867, 2005. 3
17. N. L. Roux, N. Heess, J. Shotton, and J. M. Winn. Learning a generative model of images by factoring appearance and shape. Neural Computation, 23(3):593-650, 2011. 1
18. P. Smolensky. Information processing in dynamical systems: Foundations of harmony theory. In D. E. Rumelhart and J. L. McClelland, editors, Parallel Distributed Processing, volume 1, chapter 6, pages 194-281. MIT Press, Cambridge, 1986. 2
19. J. Susskind. The Toronto Face Database. Technical report, 2011. http://aclab.ca/users/josh/TFD.html.4, 5
20. M. Svensén and C. M. Bishop. Robust bayesian mixture modelling. Neuro-computing, 64:235-252, 2005. 3
21. Y. Tang. Gated Boltzmann Machine for recognition under occlusion. In NIPS Workshop on Transfer Learning by Learning Rich Generative Models, 2010. 1
22. G. W. Taylor, R. Fergus, Y. LeCun, and C. Bregler. Convolutional learning of spatio-temporal features. In ECCV 2010. Springer, 2010. 2
23. T. Tieleman. Training restricted boltzmann machines using approximations to the likelihood gradient. In Intl. Conf. on Machine Learning, volume 307, pages 1064-1071, 2008. 4
24. T. Tieleman and G. E. Hinton. Using fast weights to improve persistent contrastive divergence. In ICML, volume 382, page 130. ACM, 2009. 6
25. M. Turk and A. P. Pentland. Eigenfaces for recognition. Journal Cognitive Neuroscience, 3(1):71-96, 1991. 6
26. C. K. I.Williams and M. K. Titsias. Greedy learning of multiple objects in images using robust statistics and factorial learning. Neural Computation, 16(5):1039-1062, May 2004. 1
27. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma. Robust face recognition via sparse representation. IEEE Trans. Pattern Analysis and Machine Intelligence, 31(2):210-227, Feb. 2009. 1
28. L. Younes. On the convergence of markovian stochastic algorithms with rapidly decreasing ergodicity rates. In Stochastics and Stochastics Models, pages 177-228, 1998. 4
29. Z. Zhou, A. Wagner, H. Mobahi, J. Wright, and Y. Ma. Face recognition with contiguous occlusion using markov random fields. In ICCV, pages 1050-1057. IEEE, 2009. 1