Corpus ID: 219687317

Rethinking the Value of Labels for Improving Class-Imbalanced Learning

@article{Yang2020RethinkingTV,
  title={Rethinking the Value of Labels for Improving Class-Imbalanced Learning},
  author={Yuzhe Yang and Zhi Xu},
  journal={ArXiv},
  year={2020},
  volume={abs/2006.07529}
}
Real-world data often exhibits long-tailed distributions with heavy class imbalance, posing great challenges for deep recognition models. We identify a persisting dilemma on the value of labels in the context of imbalanced learning: on the one hand, supervision from labels typically leads to better results than its unsupervised counterparts; on the other hand, heavily imbalanced data naturally incurs "label bias" in the classifier, where the decision boundary can be drastically altered by the… Expand
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