Learning from Noisy Labels with Distillation

ICCV, 2017.

Cited by: 195|Bibtex|Views168
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Other Links: dblp.uni-trier.de|academic.microsoft.com|arxiv.org

Abstract:

The ability of learning from noisy labels is very useful in many visual recognition tasks, as a vast amount of data with noisy labels are relatively easy to obtain. Traditionally, label noise has been treated as statistical outliers, and techniques such as importance re-weighting and bootstrapping have been proposed to alleviate the probl...More

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