Learning Deep Representation for Imbalanced Classification

@article{Huang2016LearningDR,
  title={Learning Deep Representation for Imbalanced Classification},
  author={C. Huang and Y. Li and Chen Change Loy and X. Tang},
  journal={2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  year={2016},
  pages={5375-5384}
}
  • C. Huang, Y. Li, +1 author X. Tang
  • Published 2016
  • Computer Science
  • 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Data in vision domain often exhibit highly-skewed class distribution, i.e., most data belong to a few majority classes, while the minority classes only contain a scarce amount of instances. [...] Key Result The representation learned by our approach, when combined with a simple k-nearest neighbor (kNN) algorithm, shows significant improvements over existing methods on both high-and low-level vision classification tasks that exhibit imbalanced class distribution.Expand
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