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Found 2 papers, 1 papers with code
Error-Bounded Correction of Noisy Labels
To be robust against label noise, many successful methods rely on the noisy classifiers (i. e., models trained on the noisy training data) to determine whether a label is trustworthy.
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Label Cleaning with Likelihood Ratio Test
To collect large scale annotated data, it is inevitable to introduce label noise, i. e., incorrect class labels.
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