Transforming auto-encoders

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 6791 LNCS, Issue PART 1, 2011, Pages 44-51

  Hinton, Geoffrey E ^a   Krizhevsky, Alex ^a   Wang, Sida D ^a  

^a UNIVERSITY OF TORONTO   (Canada)

Author keywords

auto encoder; Invariance; shape representation

Indexed keywords

AUTO-ENCODER; EXPLICIT REPRESENTATION; FEATURE DETECTOR; SHAPE REPRESENTATION; VISION COMMUNITIES;

COMPUTER VISION; FEATURE EXTRACTION;

NEURAL NETWORKS;

EID: 79959347463     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-21735-7_6     Document Type: Conference Paper

References (12)

1. Berkes, P., Turner, R.E., Sahani, M.: A structured model of video reproduces primary visual cortical organisation. PLoS Computational Biology 5(9), 1-16 (2009)
2. Freeman, W., Adelson, E.: The design and use of steerable filters. IEEE Transactions on Pattern Analysis and Machine Intelligence 13(9), 891-906 (1991)
3. Hinton, G.E.: Shape representation in parallel systems. In: Proc. 7th International Joint Conference on Artificial Intelligence, vol. 2, pp. 1088-1096 (1981)
4. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proceedings of the IEEE 86(11), 2278-2324 (1998)
5. Lee, H., Grosse, R., Ranganath, R., Ng, A.: Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In: Proc. 26th International Conference on Machine Learning (2009)
6. Lowe, D.G.: Object recognition from local scale-invariant features. In: Proc. International Conference on Computer Vision (1999)
7. Memisevic, R., Hinton, G.: Learning to represent spatial transformations with factored higher-order boltzmann machines. Neural Comp. 22, 1473-1492 (2010)
8. Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: Proc. 27th International Conference on Machine Learning (2010)
9. Pelli, D.G., Tillman, K.A.: The uncrowded window of object recognition. Nature Neuroscience 11, 1129-1135 (2008)
10. Ranzato, M., Huang, F., Boureau, Y., LeCun, Y.: Unsupervised learning of invariant feature hierarchies with applications to object recognition. In: Proc. Computer Vision and Pattern Recognition Conference (CVPR 2007). IEEE Press, Los Alamitos (2007)
11. Riesenhuber, M., Poggio, T.: Hierarchical models of object recognition in cortex. Nature Neuroscience 2, 1019-1025 (1999)
12. Zemel, R.S., Mozer, M.C., Hinton, G.E.: Traffic: Recognizing objects using hierarchical reference frame transformations. In: Touretzky, D.S. (ed.) Advances in Neural Information Processing Systems, pp. 266-273. Morgan Kauffman, San Mateo (1990)