Class Rectification Hard Mining for Imbalanced Deep Learning

Proceedings of the IEEE International Conference on Computer Vision

Volumn 2017-October, Issue , 2017, Pages 1869-1878

  Dong, Qi ^a   Gong, Shaogang ^a   Zhu, Xiatian ^b  

^a QUEEN MARY UNIVERSITY OF LONDON   (United Kingdom)

^b VISION SEMANTICS LTD   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION;

ATTRIBUTE CLASS; ATTRIBUTE RECOGNITION; BENCHMARK DATASETS; IMBALANCED DATA; LEARNING FRAMEWORKS; LEARNING MODELS; STATE OF THE ART; TRAINING DATA;

DEEP LEARNING;

EID: 85041895063     PISSN: 15505499     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICCV.2017.205     Document Type: Conference Paper

References (51)

1. R. Akbani, S. Kwek, and N. Japkowicz. Applying support vector machines to imbalanced datasets. In European Conference of Machine Learning, pages 39-50, 2004.
2. R. K. Ando and T. Zhang. A framework for learning predictive structures from multiple tasks and unlabeled data. The Journal of Machine Learning Research, 6 (Nov):1817-1853, 2005.
3. Y. Bengio, A. Courville, and P. Vincent. Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35 (8):1798-1828, 2013.
4. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer. Smote: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16:321-357, 2002.
5. C. Chen, A. Liaw, and L. Breiman. Using random forest to learn imbalanced data. University of California, Berkeley, pages 1-12, 2004.
6. K. Chen, S. Gong, T. Xiang, and C. Loy. Cumulative attribute space for age and crowd density estimation. In IEEE Conference on Computer Vision and Pattern Recognition, Portland, Oregon, USA, 2013.
7. Q. Chen, J. Huang, R. Feris, L. M. Brown, J. Dong, and S. Yan. Deep domain adaptation for describing people based on fine-grained clothing attributes. In IEEE Conference on Computer Vision and Pattern Recognition, pages 5315-5324, 2015.
8. S. Chopra, R. Hadsell, and Y. LeCun. Learning a similarity metric discriminatively, with application to face verification. In IEEE Conference on Computer Vision and Pattern Recognition, 2005.
9. Y. Cui, F. Zhou, Y. Lin, and S. Belongie. Fine-grained categorization and dataset bootstrapping using deep metric learning with humans in the loop. In IEEE Conference on Computer Vision and Pattern Recognition, 2016.
10. Q. Dong, S. Gong, and X. Zhu. Multi-task curriculum transfer deep learning of clothing attributes. In IEEE Winter Conference on Applications of Computer Vision, 2017.
11. C. Drummond, R. C. Holte, et al. C4. 5, class imbalance, and cost sensitivity: why under-sampling beats over-sampling. volume 11, 2003.
12. M. Everingham, S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111 (1):98-136, 2015.
13. T. Evgeniou and M. Pontil. Regularized multi-task learning. In ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 109-117, 2004.
14. P. F. Felzenszwalb, R. B. Girshick, D. McAllester, and D. Ramanan. Object detection with discriminatively trained partbased models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32 (9):1627-1645, 2010.
15. R. Feris, R. Bobbitt, L. Brown, and S. Pankanti. Attributebased people search: Lessons learnt from a practical surveillance system. In ACM International Conference on Multimedia Retrieval, page 153, 2014.
16. F. Fernández-Navarro, C. Hervás-Martínez, and P. A. Gutiérrez. A dynamic over-sampling procedure based on sensitivity for multi-class problems. Pattern Recognition, 44 (8):1821-1833, 2011.
17. S. Gong, M. Cristani, S. Yan, and C. C. Loy. Person reidentification, volume 1. Springer, 2014.
18. G. Griffin, A. Holub, and P. Perona. Caltech-256 object category dataset. 2007.
19. H. Han, W.-Y. Wang, and B.-H. Mao. Borderline-smote: a new over-sampling method in imbalanced data sets learning. In International Conference on Intelligent Computing, pages 878-887. Springer, 2005.
20. H. He and E. A. Garcia. Learning from imbalanced data. IEEE Transactions on knowledge and data engineering, 21 (9):1263-1284, 2009.
21. C. Huang, Y. Li, C. Change Loy, and X. Tang. Learning deep representation for imbalanced classification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 5375-5384, 2016.
22. J. Huang, R. S. Feris, Q. Chen, and S. Yan. Cross-domain image retrieval with a dual attribute-aware ranking network. In IEEE International Conference on Computer Vision, 2015.
23. S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv e-prints, 2015.
24. P. Jeatrakul, K. W. Wong, and C. C. Fung. Classification of imbalanced data by combining the complementary neural network and smote algorithm. In International Conference on Neural Information Processing, pages 152-159. Springer, 2010.
25. S. H. Khan, M. Bennamoun, F. Sohel, and R. Togneri. Cost sensitive learning of deep feature representations from imbalanced data. arXiv e-prints, 2015.
26. A. Krizhevsky and G. Hinton. Learning multiple layers of features from tiny images. 2009.
27. A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, pages 1097-1105, 2012.
28. M. Lin, Q. Chen, and S. Yan. Network in network. arXiv e-prints, 2013.
29. T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick. Microsoft coco: Common objects in context. In European Conference on Computer Vision, pages 740-755, 2014.
30. T.-Y. Liu. Learning to rank for information retrieval. Foundations and Trends in Information Retrieval, 3 (3):225-331, 2009.
31. Z. Liu, P. Luo, S. Qiu, X. Wang, and X. Tang. Deepfashion: Powering robust clothes recognition and retrieval with rich annotations. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1096-1104, 2016.
32. Z. Liu, P. Luo, X. Wang, and X. Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE International Conference on Computer Vision, pages 3730-3738, 2015.
33. T. Maciejewski and J. Stefanowski. Local neighbourhood extension of smote for mining imbalanced data. In IEEE International Conference on Data Mining, pages 104-111, 2011.
34. I. Mani and I. Zhang. knn approach to unbalanced data distributions: a case study involving information extraction. In Proceedings of workshop on learning from imbalanced datasets, 2003.
35. H. Oh Song, Y. Xiang, S. Jegelka, and S. Savarese. Deep metric learning via lifted structured feature embedding. In IEEE Conference on Computer Vision and Pattern Recognition, 2016.
36. M. Oquab, L. Bottou, I. Laptev, and J. Sivic. Learning and transferring mid-level image representations using convolutional neural networks. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1717-1724, 2014.
37. O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al. Imagenet large scale visual recognition challenge. International Journal of Computer Vision, 115 (3):211-252, 2015.
38. F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. In IEEE Conference on Computer Vision and Pattern Recognition, 2015.
39. A. Sharif Razavian, H. Azizpour, J. Sullivan, and S. Carlsson. Cnn features off-the-shelf: an astounding baseline for recognition. In IEEE Conference on Computer Vision and Pattern Recognition, pages 806-813, 2014.
40. W. Shen, X. Wang, Y. Wang, X. Bai, and Z. Zhang. Deepcontour: A deep convolutional feature learned by positivesharing loss for contour detection. In IEEE Conference on Computer Vision and Pattern Recognition, pages 3982-3991, 2015.
41. K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. In International Conference on Learning Representations, 2015.
42. Y. Sun, Y. Chen, X. Wang, and X. Tang. Deep learning face representation by joint identification-verification. In Advances in Neural Information Processing Systems, 2014.
43. Y. Tang, Y.-Q. Zhang, N. V. Chawla, and S. Krasser. Svms modeling for highly imbalanced classification. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 39 (1):281-288, 2009.
44. K. M. Ting. A comparative study of cost-sensitive boosting algorithms. In International Conference on Machine learning, 2000.
45. E. Ustinova and V. Lempitsky. Learning deep embeddings with histogram loss. In Advances in Neural Information Processing Systems, 2016.
46. J. Wang, Y. Song, T. Leung, C. Rosenberg, J. Wang, J. Philbin, B. Chen, and Y. Wu. Learning fine-grained image similarity with deep ranking. In IEEE Conference on Computer Vision and Pattern Recognition, 2014.
47. J. Wang, X. Zhu, S. Gong, and W. Li. Attribute recognition by joint recurrent learning of context and correlation. In IEEE International Conference on Computer Vision, 2017.
48. X. Wang and A. Gupta. Unsupervised learning of visual representations using videos. In IEEE International Conference on Computer Vision, 2015.
49. B. Zadrozny, J. Langford, and N. Abe. Cost-sensitive learning by cost-proportionate example weighting. In IEEE International Conference on Data Mining, pages 435-442, 2003.
50. N. Zhang, M. Paluri, M. Ranzato, T. Darrell, and L. Bourdev. Panda: Pose aligned networks for deep attribute modeling. In IEEE Conference on Computer Vision and Pattern Recognition, pages 1637-1644, 2014.
51. Z.-H. Zhou and X.-Y. Liu. Training cost-sensitive neural networks with methods addressing the class imbalance problem. IEEE Transactions on Knowledge and Data Engineering, 18 (1):63-77, 2006.