Weakly-Shared deep transfer networks for heterogeneous-domain knowledge propagation

MM 2015 - Proceedings of the 2015 ACM Multimedia Conference

Volumn , Issue , 2015, Pages 35-44

  Shu, Xiangbo ^a   Qi, Guo Jun ^b   Tang, Jinhui ^a   Wang, Jingdong ^c  

^a NANJING UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b UNIVERSITY OF CENTRAL FLORIDA   (United States)

^c MICROSOFT RESEARCH   (United States)

Author keywords

Cross domain label transfer; Deep transfer network; Heterogeneous domain knowledge propagation; Image classification

Indexed keywords

COMPLEX NETWORKS; IMAGE CLASSIFICATION;

COMPETITIVE PERFORMANCE; CROSS-DOMAIN; DIFFERENT DOMAINS; HETEROGENEOUS DOMAINS; NETWORK STRUCTURES; NOVEL ARCHITECTURE; STATE-OF-THE-ART METHODS; TRANSFER NETWORK;

DELAY TOLERANT NETWORKS;

EID: 84962844543     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2733373.2806216     Document Type: Conference Paper

References (37)

1. Y. Bengio. Learning deep architectures for ai. Foundations and trendsR in Machine Learning, 2(1):1-127, 2009.
2. Y. Bengio. Deep learning of representations for unsupervised and transfer learning. In ICML, pages 17-36, 2012.
3. Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle. Greedy layer-wise training of deep networks. NIPS, 19:153, 2007.
4. M. Chen, Z. Xu, F. Sha, and K. Q. Weinberger. Marginalized denoising autoencoders for domain adaptation. In ICML, pages 767-774, 2012.
5. T.-S. Chua, J. Tang, R. Hong, H. Li, Z. Luo, and Y. Zheng. Nus-wide: A real-world web image database from national university of singapore. In CIVR, page 48, 2009.
6. J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248-255, 2009.
7. J. Donahue, J. Hoffman, E. Rodner, K. Saenko, and T. Darrell. Semi-supervised domain adaptation with instance constraints. In CVPR, pages 668-675, 2013.
8. L. Duan, D. Xu, and I. W. Tsang. Learning with augmented features for heterogeneous domain adaptation. In ICML, pages 711-718, 2012.
9. F. Feng, X. Wang, and R. Li. Cross-modal retrieval with correspondence autoencoder. In ACM Multimedia, pages 7-16, 2014.
10. X. Glorot, A. Bordes, and Y. Bengio. Domain adaptation for large-scale sentiment classification: A deep learning approach. In ICML, pages 513-520, 2011.
11. G. Hinton, S. Osindero, and Y.-W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527-1554, 2006.
12. G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. R. Salakhutdinov. Improving neural networks by preventing co-Adaptation of feature detectors. arXiv, 2012.
13. Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long, R. Girshick, S. Guadarrama, and T. Darrell. Caffe: Convolutional architecture for fast feature embedding. In ACM Multimedia, pages 675-678, 2014.
14. Y.-G. Jiang, C.-W. Ngo, and S.-F. Chang. Semantic context transfer across heterogeneous sources for domain adaptive video search. In ACM Multimedia, pages 155-164, 2009.
15. M. Kan, S. Shan, H. Chang, and X. Chen. Stacked progressive auto-encoders (spae) for face recognition across poses. In CVPR, pages 1883-1890, 2014.
16. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1097-1105, 2012.
17. H. Lee, C. Ekanadham, and A. Y. Ng. Sparse deep belief net model for visual area v2. In NIPS, pages 873-880, 2008.
18. M. Long, J. Wang, G. Ding, J. Sun, and P. S. Yu. Transfer joint matching for unsupervised domain adaptation. In CVPR, pages 1410-1417, 2014.
19. J. Ngiam, Z. Chen, S. A. Bhaskar, P. W. Koh, and A. Y. Ng. Sparse filtering. In NIPS, pages 1125-1133, 2011.
20. J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng. Multimodal deep learning. In ICML, pages 689-696, 2011.
21. J. Ni, Q. Qiu, and R. Chellappa. Subspace interpolation via dictionary learning for unsupervised domain adaptation. In CVPR, pages 692-699, 2013.
22. X. Ou, L. Yan, H. Ling, C. Liu, and M. Liu. Inductive transfer deep hashing for image retrieval. In ACM Multimedia, pages 969-972, 2014.
23. S. J. Pan and Q. Yang. A survey on transfer learning. TKDE, 22(10):1345-1359, 2010.
24. G.-J. Qi, C. Aggarwal, and T. Huang. Towards semantic knowledge propagation from text corpus to web images. In WWW, pages 297-306, 2011.
25. S. Rifai, P. Vincent, X. Muller, X. Glorot, and Y. Bengio. Contractive auto-encoders: Explicit invariance during feature extraction. In ICML, pages 833-840, 2011.
26. S. D. Roy, T. Mei, W. Zeng, and S. Li. Socialtransfer: cross-domain transfer learning from social streams for media applications. In ACM Multimedia, pages 649-658, 2012.
27. R. Salakhutdinov and G. E. Hinton. Deep boltzmann machines. In AISTATS, pages 448-455, 2009.
28. R. Salakhutdinov, A. Mnih, and G. Hinton. Restricted boltzmann machines for collaborative filtering. In ICML, pages 791-798, 2007.
29. R. Socher, M. Ganjoo, C. D. Manning, and A. Ng. Zero-shot learning through cross-modal transfer. In NIPS, pages 935-943, 2013.
30. K. Sohn, W. Shang, and H. Lee. Improved multimodal deep learning with variation of information. In NIPS, pages 2141-2149, 2014.
31. N. Srivastava and R. Salakhutdinov. Multimodal learning with deep boltzmann machines. In NIPS, pages 2222-2230, 2012.
32. Y. Taigman, M. Yang, M. Ranzato, and L. Wolf. Deepface: Closing the gap to human-level performance in face verification. In CVPR, pages 1701-1708, 2014.
33. P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol. Extracting and composing robust features with denoising autoencoders. In ICML, pages 1096-1103, 2008.
34. W. Wang, Z. Cui, H. Chang, S. Shan, and X. Chen. Deeply coupled auto-encoder networks for cross-view classification. arXiv, 2014.
35. P. Xu, M. Ye, X. Li, Q. Liu, Y. Yang, and J. Ding. Dynamic background learning through deep auto-encoder networks. In ACM Multimedia, pages 107-116, 2014.
36. X. Zhang, F. X. Yu, S.-F. Chang, and S. Wang. Deep transfer network: Unsupervised domain adaptation. arXiv, 2015.
37. Y. Zhu, Y. Chen, Z. Lu, S. J. Pan, G.-R. Xue, Y. Yu, and Q. Yang. Heterogeneous transfer learning for image classification. In AAAI, 2011.