Domain generalization for object recognition with multi-task autoencoders

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2015 International Conference on Computer Vision, ICCV 2015, Issue , 2015, Pages 2551-2559

  Ghifary, Muhammad ^a   Kleijn, W Bastiaan ^a   Zhang, Mengjie ^a   Balduzzi, David ^a  

^a VICTORIA UNIVERSITY OF WELLINGTON   (New Zealand)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; BENCHMARKING; COMPUTER VISION; IMAGE RECOGNITION; LEARNING SYSTEMS;

AUTO ENCODERS; GENERALIZATION PERFORMANCE; MULTIPLE DATA SETS; NATURALLY OCCURRING; NOISY VERSIONS; ORIGINAL IMAGES; STATE-OF-THE-ART ALGORITHMS; TRAINING DATA;

OBJECT RECOGNITION;

EID: 84973915327     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2015.293     Document Type: Conference Paper

References (38)

1. A. Argyriou, T. Evgeniou, and M. Pontil. Convex Multi-Task Feature Learning. Machine Learning, 73 (3): 243-272, 2008. 2
2. M. Baktashmotlagh, M. T. Harandi, B. C. Lovell, and M. Salzmann. Domain Adaptation on the Statistical Manifold. In CVPR, pages 2481-2488, 2014. 2
3. J. Baxter. A Model of Inductive Bias Learning. Journal of Artificial Intelligence Research, 12: 149-198, 2000. 2
4. Y. Bengio, A. C. Courville, and P. Vincent. Representation Learning: A Review and New Perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (8): 1798-1828, 2013. 2
5. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle. Greedy Layer-Wise Training of Deep Networks. In NIPS, pages 153-160, 2007. 1, 2, 5
6. G. Blanchard, G. Lee, and C. Scott. Generalizing from Several Related Classification Tasks to a New Unlabeled Sample. In NIPS, volume 1, pages 2178-2186, 2011. 1, 2
7. H. Bourlard and Y. Kamp. Auto-Association by Multilayer Perceptrons and Singular Value Decomposition. Biological Cybernetics, 59: 291-294, 1988. 2
8. R. Caruana. Multitask Learning. Machine Learning, 28: 41-75, 1997. 1, 2, 3
9. M. Chen, Z. Xu, K. Weinberger, and F. Sha. Marginalized Denoising Autoencoders for Domain Adaptation. In ICML, pages 767-774, 2012. 2
10. M. J. Choi, J. J. Lim, A. Torralba, and A. S. Willsky. Exploiting hierarchical context on a large database of object categories. In CVPR, pages 129-136, 2010. 4
11. D. Ciresan, U. Meier, and J. Schmidhuber. Multi-column deep neural network for image classification. In CVPR, pages 3642-3649, 2012. 1
12. K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. JMLR, 2: 265-292, 2001. 5
13. J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell. DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition. In ICML, pages 647-655, 2014. 2, 7
14. M. Everingham, L. Van-Gool, C. K. I. Williams, J. Winn, and A. Zisserman. The PASCAL Visual Object Classes Challenge 2007 (VOC2007) Results, 2007. 1, 4
15. R.-E. Fan, K.-W. Chang, C.-J. Hsieh, X.-R. Wang, and C.-J. Lin. LIBLINEAR: A Library for Large Linear Classification. JMLR, 9: 1871-1874, 2008. 5
16. C. Fang, Y. Xu, and D. N. Rockmore. Unbiased Metric Learning: On the Utilization of Multiple Datasets and Web Images for Softening Bias. In ICCV, pages 1657-1664, 2013. 2, 7
17. M. Ghifary, W. B. Kleijn, and M. Zhang. Deep hybrid networks with good out-of-sample object recognition. In ICASSP, pages 5437-5441, 2014. 2
18. B. Gong, K. Grauman, and F. Sha. Reshaping Visual Datasets for Domain Adaptation. In NIPS, pages 1286-1294, 2013. 1
19. B. Gong, Y. Shi, F. Sha, and K. Grauman. Geodesic Flow Kernel for Unsupervised Domain Adaptation. In CVPR, pages 2066-2073, 2012. 7
20. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. Technical report, California Inst. of Tech., 2007. 4
21. A. Khosla, T. Zhou, T. Malisiewicz, A. Efros, and A. Torralba. Undoing the Damage of Dataset Bias. In ECCV, volume I, pages 158-171, 2012. 2, 7
22. A. Krizhevsky. Learning Multiple Layers of Features from Tiny Images. Master's thesis, Department of Computer Science, University of Toronto, Apr. 2009. 1
23. A. Krizhevsky, I. Sutskever, and G. E. Hinton. ImageNet Classification with Deep Convolutional Neural Networks. In NIPS, volume 25, pages 1106-1114, 2012. 1, 6
24. Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradientbased learning applied to document recognition. In Proceedings of the IEEE, volume 86, pages 2278-2324, 1998. 4
25. B. Leibe and B. Schiele. Analyzing appearance and contour based methods for object categorization. In CVPR, pages 409-415, 2003. 4
26. K. Muandet, D. Balduzzi, and B. Schölkopf. Domain Generalization via Invariant Feature Representation. In ICML, pages 10-18, 2013. 1, 2, 5
27. S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio. Contractive Auto-Encoders: Explicit Invariance During Feature Extraction. In ICML, number 1, pages 833-840, 2011. 2, 5, 6
28. D. Rumelhart, G. Hinton, and R. Williams. Parallel Distributed Processing. I: Foundations. MIT Press, 1986. 1
29. D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Nature, 323: 533-536, 1986. 2
30. B. C. Russell, A. Torralba, K. P. Murphy, and W. T. Freeman. LabelMe: A database and web-based tool for image annotation. In IJCV, volume 77, pages 157-173. 2008. 4
31. K. Saenko, B. Kulis, M. Fritz, and T. Darrell. Adapting Visual Cateogry Models to New Domains. In ECCV, pages 213-226, 2010. 1, 2, 4, 7
32. I. Sutskever, O. Vinyals, and Q. Le. Sequence to Sequence Learning with Neural Networks. In NIPS, 2014. 1
33. Y. Tang and C. Eliasmith. Deep networks for robust visual recognition. In ICML, pages 1055-1062, 2010. 2
34. S. Thrun. Is learning the n-th thing any easier than learning the first? In NIPS, pages 640-646, 1996. 1, 2
35. A. Torralba and A. Efros. Unbiased look at dataset bias. In CVPR, pages 1521-1528, 2011. 1, 8
36. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and Composing Robust Features with Denoising Autoencoders. In ICML, 2008. 1
37. P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion. Journal of Machine Learning Research, 11: 3371-3408, 2010. 2, 3, 5, 6
38. Z. Xu, W. Li, L. Niu, and D. Xu. Exploiting Low-Rank Structure from Latent Domains for Domain Generalization. In ECCV, pages 628-643, 2014. 2, 7, 8