Learning deep representation for imbalanced classification
Refereed conference paper presented and published in conference proceedings


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摘要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. To mitigate this issue, contemporary classification methods based on deep convolutional neural network (CNN) typically follow classic strategies such as class re-sampling or cost-sensitive training. In this paper, we conduct extensive and systematic experiments to validate the effectiveness of these classic schemes for representation learning on class-imbalanced data. We further demonstrate that more discriminative deep representation can be learned by enforcing a deep network to maintain both intercluster and inter-class margins. This tighter constraint effectively reduces the class imbalance inherent in the local data neighborhood. We show that the margins can be easily deployed in standard deep learning framework through quintuplet instance sampling and the associated triple-header hinge loss. 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.
著者Huang C., Li Y., Loy C.C., Tang X.
會議名稱2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016
會議開始日26.06.2016
會議完結日01.07.2016
會議地點Las Vegas
會議國家/地區美國
出版年份2016
月份1
日期1
卷號2016-January
頁次5375 - 5384
國際標準書號9781467388511
國際標準期刊號1063-6919
語言英式英語

上次更新時間 2020-31-07 於 23:12