  Deep neural networks are known to be annotation-hungry. Numerous efforts have
been devoted to reducing the annotation cost when learning with deep networks.
Two prominent directions include learning with noisy labels and semi-supervised
learning by exploiting unlabeled data. In this work, we propose DivideMix, a
novel framework for learning with noisy labels by leveraging semi-supervised
learning techniques. In particular, DivideMix models the per-sample loss
distribution with a mixture model to dynamically divide the training data into
a labeled set with clean samples and an unlabeled set with noisy samples, and
trains the model on both the labeled and unlabeled data in a semi-supervised
manner. To avoid confirmation bias, we simultaneously train two diverged
networks where each network uses the dataset division from the other network.
During the semi-supervised training phase, we improve the MixMatch strategy by
performing label co-refinement and label co-guessing on labeled and unlabeled
samples, respectively. Experiments on multiple benchmark datasets demonstrate
substantial improvements over state-of-the-art methods. Code is available at
https://github.com/LiJunnan1992/DivideMix .
