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Learning from Noisy Labels with No Change to the Training Process
2021
International Conference on Machine Learning
There has been much interest in recent years in developing learning algorithms that can learn accurate classifiers from data with noisy labels. A widely-studied noise model is that of classconditional noise (CCN), wherein a label y is flipped to a label y with some associated noise probability that depends on both y and y. In the multiclass setting, all previously proposed algorithms under the CCN model involve changing the training process, by introducing a 'noisecorrection' to the surrogate
dblp:conf/icml/ZhangL021
fatcat:nma4wppejvfcfphxbhmjj57kra