Clustering learning for robotic vision

1st International Conference on Learning Representations, ICLR 2013 - Workshop Track Proceedings

Volumn , Issue , 2013, Pages

  Culurciello, Eugenio ^a   Bates, Jordan ^a   Dundar, Aysegul ^a   Perez Carrasco, J A ^b   Farabet, Clement ^c  

^a PURDUE UNIVERSITY   (United States)

^b UNIVERSITY OF SEVILLE   (Spain)

^c NEW YORK UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; DEEP NEURAL NETWORKS; FEEDFORWARD NEURAL NETWORKS; LARGE DATASET; LEARNING ALGORITHMS; ROBOTICS;

CLUSTERING LEARNING; COMPLEX DATASETS; FEED-FORWARD; LARGE NETWORKS; NETWORK FILTERS; OBJECT TRACKING; ROBOTIC VISION; STATIC IMAGES;

MULTILAYER NEURAL NETWORKS;

EID: 85083951967     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (37)

1. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, November 1998.
2. R. Hadsell, S. Chopra, and Y. LeCun. Dimensionality reduction by learning an invariant mapping. In Proc. Computer Vision and Pattern Recognition Conference (CVPR’06). IEEE Press, 2006.
3. K. Gregor, A. Szlam, and Y. LeCun. Structured sparse coding via lateral inhibition. In Advances in Neural Information Processing Systems (NIPS 2011), volume 24, 2011.
4. M. Riesenhuber and T. Poggio. Hierarchical models of object recognition in cortex. Nature neuroscience, 2:10191025, 1999.
5. T. Serre, A. Oliva, and T. Poggio. A feedforward architecture accounts for rapid categorization. Proceedings of the National Academy of Sciences, 104(15):64246429, 2007.
6. T. Serre and T. Poggio. A neuromorphic approach to computer vision. Communications of the ACM, 53(10):5461, 2010.
7. K. Jarrett, K. Kavukcuoglu, M. Ranzato, and Y. LeCun. What is the best multi-stage architecture for object recognition? In Proc. International Conference on Computer Vision (ICCV’09). IEEE, 2009.
8. Y. LeCun, K. Kavukvuoglu, and C. Farabet. Convolutional networks and applications in vision. In Proc. International Symposium on Circuits and Systems (ISCAS’10). IEEE, 2010.
9. Y. Boureau, J. Ponce, and Y. LeCun. A theoretical analysis of feature pooling in vision algorithms. In Proc. International Conference on Machine learning (ICML’10), 2010.
10. A. Karpathy. Lessons learned from manually classifying CIFAR-10. http://karpathy.ca/myblog/2011/04/27/lessons-learned-from-manually-classifying-cifar-10-with-code/, April 2011.
11. A. Torralba and A. A. Efros. Unbiased look at dataset bias. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, page 15211528, 2011.
12. X. Hou, A. Yuille, and H. Koch. A meta-theory of boundary detection benchmarks. In NIPS Workshop on Human Computation for Science and Computational Sustainability, 2012.
13. B. A. Olshausen and D. J. Field. Emergence of simple-cell receptive field properties by learning a sparse code for natural images. Nature, 381(6583):607609, 1996.
14. A. Hyvärinen and E. Oja. Independent component analysis: algorithms and applications. Neural networks, 13(4):411430, 2000.
15. G. E. Hinton, S. Osindero, and Y. W. Teh. A fast learning algorithm for deep belief nets. Neural computation, 18(7):15271554, 2006.
16. P. Vincent, H. Larochelle, Y. Bengio, and P. A. Manzagol. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, page 10961103, 2008.
17. A. Coates, H. Lee, and A. Y. Ng. An analysis of single-layer networks in unsupervised feature learning. AISTATS, 14, 2011.
18. R. Collobert, K. Kavukcuoglu, and C. Farabet. Torch7: A matlab-like environment for machine learning. In NIPS Workshop BigLearn, 2011.
19. A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. Master’s thesis, Department of Computer Science, University of Toronto, 2009.
20. Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng. Reading digits in natural images with unsupervised feature learning. In NIPS Workshop on Deep Learning and Unsupervised Feature Learning, volume 2011, 2011.
21. D. Martin, C. Fowlkes, D. Tal, and J. Malik. A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In Computer Vision, 2001. ICCV 2001. Proceedings. Eighth IEEE International Conference on, volume 2, page 416423, 2001.
22. Z. Kalal, K. Mikolajczyk, and J. Matas. Tracking-learning-detection. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(7):14091422, 2012.
23. B. A. Wandell. Foundations of vision. Sinauer Associates, 1995.
24. P. Sermanet, S. Chintala, and Yann LeCun. Convolutional neural networks applied to house numbers digit classification. In International Conference on Pattern Recognition (ICPR 2012), 2012.
25. C. Farabet, C. Couprie, L. Najman, and Y. LeCun. Scene parsing with multiscale feature learning, purity trees, and optimal covers. arXiv preprint arXiv:1202.2160, 2012.
26. A. Coates and A. Y. Ng. Selecting receptive fields in deep networks. Advances in Neural Information Processing Systems, 24:25282536, 2011.
27. D. Ciresan, U. Meier, and J. Schmidhuber. Multi-column deep neural networks for image classification. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, page 36423649, 2012.
28. Z. Kalal, J. Matas, and K. Mikolajczyk. P-n learning: Bootstrapping binary classifiers by structural constraints. Conference on Computer Vision and Pattern Recognition, 2010.
29. Z. Kalal. TLD dataset. http://info.ee.surrey.ac.uk/Personal/Z.Kalal/TLD/TLD_dataset.pdf, 2010.
30. A. Dundar, J. Jin, and E. Culurciello. Visual tracking with similarity matching ratio. arXiv preprint arXiv:1209.2696, 2012.
31. R. Socher, B. Huval, B. Bhat, C. D. Manning, and A. Y. Ng. Convolutional-recursive deep learning for 3D object classification. Advances in Neural Information Processing Systems, 2012.
32. A. Krizhevsky, I. Sutskever, and G. Hinton. ImageNet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 2012.
33. C. Farabet, B. Martini, B. Corda, P. Akselrod, E. Culurciello, and Y. LeCun. Neuflow: A run-time reconfigurable dataflow processor for vision. In Computer Vision and Pattern Recognition Workshops (CVPRW), 2011 IEEE Computer Society Conference on, page 109116, 2011.
34. T. D. Sanger. Optimal unsupervised learning in a single-layer linear feedforward neural network. Neural networks, 2(6):459473, 1989.
35. P. Földiák. Forming sparse representations by local anti-hebbian learning. Biological cybernetics, 64(2):165170, 1990.
36. E. Oja. Principal components, minor components, and linear neural networks. Neural Networks, 5(6):927935, 1992.
37. A. J. Bell and T. J. Sejnowski. The independent components of natural scenes are edge filters. Vision research, 37(23):3327, 1997.