Recent semi-supervised learning methods use pseudo supervision as core idea,
especially self-training methods that generate pseudo labels. However, pseudo
labels are unreliable. Self-training methods usually rely on single model
prediction confidence to filter low-confidence pseudo labels, thus remaining
high-confidence errors and wasting many low-confidence correct labels. In this
paper, we point out it is difficult for a model to counter its own errors.
Instead, leveraging inter-model disagreement between different models is a key
to locate pseudo label errors. With this new viewpoint, we propose mutual
training between two different models by a dynamically re-weighted loss
function, called Dynamic Mutual Training (DMT). We quantify inter-model
disagreement by comparing predictions from two different models to dynamically
re-weight loss in training, where a larger disagreement indicates a possible
error and corresponds to a lower loss value. Extensive experiments show that
DMT achieves state-of-the-art performance in both image classification and
semantic segmentation. Our codes are released at
this https URL .

本文介绍了一种互动训练（Dynamic Mutual Training，DMT）的方法，可以通过两种不同模型之间的互动来确定伪标签的错误，从而提高半监督学习的性能。在图像分类和语义分割任务中，DMT 均取得了最新的表现。