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
this https URL .

提出了一种名为 DivideMix 的新型深度学习框架，通过利用半监督学习技术来将训练数据动态分成一个包含清晰样本的标记集合和一个包含噪声样本的未标记集合，并在半监督的方式下同时对标记与未标记数据进行训练，使用 MixMatch 策略在标记和未标记样本上分别执行标记共修整和标记共猜测以进行标签协同改进。在多个基准数据集上的实验显示出 DivideMix 比现有最先进的方法具有显着的改进。