Metric learning with adaptive density discrimination

4th International Conference on Learning Representations, ICLR 2016 - Conference Track Proceedings

Volumn , Issue , 2016, Pages

  Rippel, Oren ^a   Paluri, Manohar ^a   Dollar, Piotr ^a   Bourdev, Lubomir ^b  

^a FACEBOOK AI RESEARCH   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASS DISTRIBUTIONS; CLASSIFICATION ALGORITHM; CLASSIFICATION RESULTS; COMPUTATIONAL PERFORMANCE; DISTANCE METRIC LEARNING; RECOVERY PROPERTIES; REPRESENTATION SPACE; VISUAL RECOGNITION;

CLASSIFICATION (OF INFORMATION);

EID: 85083950568     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (31)

1. Angelova, Anelia and Long, Philip M. Benchmarking large-scale fine-grained categorization. In IEEE Winter Conference on Applications of Computer Vision, Steamboat Springs, CO, USA, March 24-26, 2014, pp. 532–539, 2014. doi: 10.1109/WACV.2014.6836056.
2. Angelova, Anelia and Zhu, Shenghuo. Efficient object detection and segmentation for fine-grained recognition. In 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, June 23-28, 2013, pp. 811–818, 2013. doi: 10.1109/CVPR.2013.110.
3. Arthur, David and Vassilvitskii, Sergei. K-means++: The advantages of careful seeding. In Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA’07, pp. 1027–1035, Philadelphia, PA, USA, 2007. Society for Industrial and Applied Mathematics. ISBN 978-0-898716-24-5.
4. Chopra, Sumit, Hadsell, Raia, and LeCun, Yann. Learning a similarity metric discriminatively, with application to face verification. In Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05) - Volume 1 - Volume 01, CVPR’05, pp. 539–546, Washington, DC, USA, 2005. IEEE Computer Society. ISBN 0-7695-2372-2. doi: 10.1109/CVPR.2005.202.
5. Donahue, Jeff, Jia, Yangqing, Vinyals, Oriol, Hoffman, Judy, Zhang, Ning, Tzeng, Eric, and Darrell, Trevor. Decaf: A deep convolutional activation feature for generic visual recognition. 2013.
6. Gavves, E., Fernando, B., Snoek, C. G. M., Smeulders, A. W. M., and Tuytelaars, T. Fine-grained categorization by alignments. In IEEE International Conference on Computer Vision, 2013.
7. Gavves, Efstratios, Fernando, Basura, Snoek, CeesG.M., Smeulders, ArnoldW.M., and Tuytelaars, Tinne. Local alignments for fine-grained categorization. International Journal of Computer Vision, 111(2):191–212, 2015. ISSN 0920-5691. doi: 10.1007/s11263-014-0741-5.
8. Globerson, Amir and Roweis, Sam T. Metric learning by collapsing classes. In Weiss, Y., Schölkopf, B., and Platt, J.C. (eds.), Advances in Neural Information Processing Systems 18, pp. 451–458. MIT Press, 2006. URL http://papers.nips.cc/paper/2947-metric-learning-by-collapsing-classes.pdf.
9. Goldberger, Jacob, Roweis, Sam, Hinton, Geoff, and Salakhutdinov, Ruslan. Neighbourhood components analysis. In Advances in Neural Information Processing Systems 17, pp. 513–520. MIT Press, 2004.
10. Hadsell, Raia, Chopra, Sumit, and Lecun, Yann. Dimensionality reduction by learning an invariant mapping. In In Proc. Computer Vision and Pattern Recognition Conference CVPR06. IEEE Press, 2006.
11. Ioffe, Sergey and Szegedy, Christian. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015, pp. 448–456, 2015.
12. Khosla, Aditya, Jayadevaprakash, Nityananda, Yao, Bangpeng, and Fei-Fei, Li. Novel dataset for fine-grained image categorization. In First Workshop on Fine-Grained Visual Categorization, IEEE Conference on Computer Vision and Pattern Recognition, Colorado Springs, CO, June 2011.
13. Mensink, Thomas, Verbeek, Jakob, Perronnin, Florent, and Csurka, Gabriela. Distance-based image classification: Generalizing to new classes at near zero cost. Transactions on Pattern Analysis and Machine Intelligence (PAMI), 2013.
14. Min, Martin Renqiang, van der Maaten, Laurens, Yuan, Zineng, Bonner, Anthony J., and Zhang, Zhaolei. Deep supervised t-distributed embedding. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), June 21-24, 2010, Haifa, Israel, pp. 791–798, 2010.
15. Murray, Naila and Perronnin, Florent. Generalized max pooling. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014, pp. 2473–2480, 2014. doi: 10.1109/CVPR.2014.317.
16. Nilsback, M-E. and Zisserman, A. Automated flower classification over a large number of classes. In Proceedings of the Indian Conference on Computer Vision, Graphics and Image Processing, Dec 2008.
17. Norouzi, Mohammad, Fleet, David, and Salakhutdinov, Ruslan R. Hamming distance metric learning. In Pereira, F., Burges, C.J.C., Bottou, L., and Weinberger, K.Q. (eds.), Advances in Neural Information Processing Systems 25, pp. 1061–1069. Curran Associates, Inc., 2012.
18. Parkhi, O. M., Vedaldi, A., Zisserman, A., and Jawahar, C. V. Cats and dogs. In IEEE Conference on Computer Vision and Pattern Recognition, 2012.
19. Qian, Qi, Jin, Rong, Zhu, Shenghuo, and Lin, Yuanqing. Fine-grained visual categorization via multi-stage metric learning. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
20. Russakovsky, Olga and Fei-Fei, Li. Attribute learning in large-scale datasets. In European Conference of Computer Vision (ECCV), International Workshop on Parts and Attributes, 2010.
21. Russakovsky, Olga, Deng, Jia, Su, Hao, Krause, Jonathan, Satheesh, Sanjeev, Ma, Sean, Huang, Zhiheng, Karpathy, Andrej, Khosla, Aditya, Bernstein, Michael, Berg, Alexander C., and Fei-Fei, Li. ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), pp. 1–42, April 2015. doi: 10.1007/s11263-015-0816-y.
22. Salakhutdinov, Ruslan and Hinton, Geoffrey E. Learning a nonlinear embedding by preserving class neighbourhood structure. In Meila, Marina and Shen, Xiaotong (eds.), Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS-07), volume 2, pp. 412–419. Journal of Machine Learning Research - Proceedings Track, 2007.
23. Schroff, Florian, Kalenichenko, Dmitry, and Philbin, James. Facenet: A unified embedding for face recognition and clustering. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015.
24. Sharif Razavian, Ali, Azizpour, Hossein, Sullivan, Josephine, and Carlsson, Stefan. Cnn features off-the-shelf: An astounding baseline for recognition. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2014.
25. Snoek, Jasper, Rippel, Oren, Swersky, Kevin, Kiros, Ryan, Satish, Nadathur, Sundaram, Narayanan, Patwary, Md. Mostofa Ali, Prabhat, and Adams, Ryan P. Scalable bayesian optimization using deep neural networks. In International Conference on Machine Learning, 2015.
26. Szegedy, Christian, Liu, Wei, Jia, Yangqing, Sermanet, Pierre, Reed, Scott, Anguelov, Dragomir, Erhan, Dumitru, Vanhoucke, Vincent, and Rabinovich, Andrew. Going deeper with convolutions. In CVPR 2015, 2015.
27. van der Maaten, L.J.P. and Hinton, G.E. Visualizing high-dimensional data using t-sne. 2008.
28. Verma, Nakul, Mahajan, Dhruv, Sellamanickam, Sundararajan, and Nair, Vinod. Learning hierarchical similarity metrics. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pp. 2280–2287. IEEE, 2012.
29. Wang, Jiang, Song, Yang, Leung, Thomas, Rosenberg, Chuck, Wang, Jingbin, Philbin, James, Chen, Bo, and Wu, Ying. Learning fine-grained image similarity with deep ranking. In Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference on, pp. 1386–1393. IEEE, 2014.
30. Weinberger, Kilian Q. and Saul, Lawrence K. Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res., 10:207–244, June 2009. ISSN 1532-4435.
31. Xie, Saining, Yang, Tianbao, Wang, Xiaoyu, and Lin, Yuanqing. Hyper-class augmented and regularized deep learning for fine-grained image classification. In IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2015, Boston, MA, USA, June 7-12, 2015, pp. 2645–2654, 2015. doi: 10.1109/CVPR.2015.7298880.