Semi-supervised learning aims to boost the accuracy of a model by exploring
unlabeled images. The state-of-the-art methods are consistency-based which
learn about unlabeled images by encouraging the model to give consistent
predictions for images under different augmentations. However, when applied to
pose estimation, the methods degenerate and predict every pixel in unlabeled
images as background. This is because contradictory predictions are gradually
pushed to the background class due to highly imbalanced class distribution. But
this is not an issue in supervised learning because it has accurate labels.
This inspires us to stabilize the training by obtaining reliable pseudo labels.
Specifically, we learn two networks to mutually teach each other. In
particular, for each image, we compose an easy-hard pair by applying different
augmentations and feed them to both networks. The more reliable predictions on
easy images in each network are used to teach the other network to learn about
the corresponding hard images. The approach successfully avoids degeneration
and achieves promising results on public datasets. The source code and
pretrained models have been released at
this https URL

该研究介绍了一种稳定半监督学习方法的做法，该方法通过相互教学的方式学习可靠的伪标签，避免了训练过程中出现的高度不平衡的类分布问题。