Deconvolutional networks

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

Volumn , Issue , 2010, Pages 2528-2535

  Zeiler, Matthew D ^a   Krishnan, Dilip ^a   Taylor, Graham W ^a   Fergus, Rob ^a  

^a POLYTECHNIC UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANALYSIS AND SYNTHESIS; EDGE INFORMATION; FEATURE DETECTOR; FEATURE SETS; IMAGE DATA; IMAGE REPRESENTATIONS; LEARNING FRAMEWORKS; MID-LEVEL CUE; SPARSITY CONSTRAINTS;

COMPUTER VISION;

EID: 77956001004     PISSN: 10636919     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CVPR.2010.5539957     Document Type: Conference Paper

References (31)

1. Y. Amit and D. Geman. A computational model for visual selection. Neural Computation, 11(7):1691-1715, 1999.
2. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle. Greedy layerwise training of deep networks. In NIPS, pages 153-160, 2007.
3. S. Chen, D. Donoho, and M. Saunders. Atomic decomposition by basis pursuit. SIAM J. Sci Comp., 20(1):33-61, 1999.
4. S. Fidler, M. Boben, and A. Leonardis. Similarity-based cross-layered hierarchical representation for object categorization. In CVPR, 2008.
5. S. Fidler and A. Leonardis. Towards scalable representations of object categories: Learning a hierarchy of parts. In CVPR, 2007.
6. D. Geman and Y. C. Nonlinear image recovery with half-quadratic regularization. PAMI, 4:932-946, 1995.
7. C. E. Guo, S. C. Zhu, and Y. N. Wu. Primal sketch: Integrating texture and structure. CVIU, 106:5-19, April 2007.
8. G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algorithm for deep belief nets. Neural Comput., 18(7):1527-1554, 2006.
9. K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In ICCV, 2009.
10. Y. Jin and S. Geman. Context and hierarchy in a probabilistic image model. In CVPR, volume 2, pages 2145-2152, 2006.
11. D. Krishnan and R. Fergus. Analytic Hyper-Laplacian Priors for Fast Image Deconvolution. In NIPS, 2009.
12. S. Lazebnik, C. Schmid, and J. Ponce. Beyond bags of features: Spatial pyramid matching for recognizing natural scene categories. In CVPR, 2006.
13. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. IEEE, 86(11):2278-2324, 1998.
14. H. Lee, A. Battle, R. Raina, and A. Y. Ng. Efficient sparse coding algorithms. In NIPS, pages 801-808, 2007.
15. H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In ICML, pages 609-616, 2009.
16. J. Mairal, F. Bach, J. Ponce, and G. Sapiro. Online dictionary learning for sparse coding. In ICML, pages 689-696, 2009.
17. J. Mairal, F. Bach, J. Ponce, G. Sapiro, and A. Zisserman. Supervised dictionary learning. In NIPS, 2008.
18. D. Marr. Vision. Freeman, San Francisco, 1982.
19. B. A. Olshausen and D. J. Field. Sparse coding with an overcomplete basis set: A strategy employed by V1? Vision Research, 37(23):3311-3325, 1997.
20. R. Raina, A. Battle, H. Lee, B. Packer, and A. Ng. Self-taught learning: Transfer learning from unlabeled data. In ICML, 2007.
21. M. Ranzato, Y. Boureau, and Y. LeCun. Sparse feature learning for deep belief networks. In NIPS. MIT Press, 2008.
22. M. Ranzato, C. S. Poultney, S. Chopra, and Y. LeCun. Efficient learning of sparse representations with an energy-based model. In NIPS, pages 1137-1144, 2006.
23. M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11):1019-1025, 1999.
24. T. Serre, L. Wolf, and T. Poggio. Object recognition with features inspired by visual cortex. In CVPR, 2005.
25. Z. W. Tu and S. C. Zhu. Parsing images into regions, curves, and curve groups. IJCV, 69(2):223-249, August 2006.
26. P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, pages 1096-1103, 2008.
27. Y. Wang, J. Yang, W. Yin, and Y. Zhang. A new alternating minimization algorithm for total variation image reconstruction. SIAM J. Imag. Sci., 1(3):248-272, 2008.
28. J. Yang, K. Yu, Y. Gong, and T. Huang. Linear spatial pyramid matching using sparse coding for image classification. In CVPR, 2009.
29. H. Zhang, A. C. Berg, M. Maire, and J. Malik. Svm-knn: Discriminative nearest neighbor classification for visual category recognition. In CVPR, 2006.
30. L. Zhu, Y. Chen, and A. L. Yuille. Learning a hierarchical deformable template for rapid deformable object parsing. PAMI, March 2009.
31. S. Zhu and D. Mumford. A stochastic grammar of images. Foundations and Trends in Comp. Graphics and Vision, 2(4):259-362, 2006.