Regularly Truncated M-Estimators for Learning With Noisy Labels
Regularly Truncated M-Estimators for Learning With Noisy Labels
The sample selection approach is very popular in learning with noisy labels. As deep networks "learn pattern first", prior methods built on sample selection share a similar training procedure: the small-loss examples can be regarded as clean examples and used for helping generalization, while the large-loss examples are treated as …