Regroup Median Loss for Combating Label Noise
Fengpeng Li Kemou Li Jinyu Tian Jiantao Zhou
The deep model training procedure requires large-scale datasets of annotated
data. Due to the difficulty of annotating a large number of samples, label
noise caused by incorrect annotations is inevitable, resulting in low model
performance and poor model generalization. To combat label noise, current
methods usually select clean samples based on the small-loss criterion and use
these samples for training. Due to some noisy samples similar to clean ones,
these small-loss criterion-based methods are still affected by label noise. To
address this issue, in this work, we propose Regroup Median Loss (RML) to
reduce the probability of selecting noisy samples and correct losses of noisy
samples. RML randomly selects samples with the same label as the training
samples based on a new loss processing method. Then, we combine the stable mean
loss and the robust median loss through a proposed regrouping strategy to
obtain robust loss estimation for noisy samples. To further improve the model
performance against label noise, we propose a new sample selection strategy and
build a semi-supervised method based on RML. Compared to state-of-the-art
methods, for both the traditionally trained and semi-supervised models, RML
achieves a significant improvement on synthetic and complex real-world
datasets. The source code of the paper has been released.