Learning from Noisy Labels with DistillationEI

    Cited by: 116|Bibtex|18|

    ICCV, 2017.

    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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