  Recently, significant progress has been made on semantic segmentation.
However, the success of supervised semantic segmentation typically relies on a
large amount of labelled data, which is time-consuming and costly to obtain.
Inspired by the success of semi-supervised learning methods in image
classification, here we propose a simple yet effective semi-supervised learning
framework for semantic segmentation. We demonstrate that the devil is in the
details: a set of simple design and training techniques can collectively
improve the performance of semi-supervised semantic segmentation significantly.
Previous works [3, 27] fail to employ strong augmentation in pseudo label
learning efficiently, as the large distribution change caused by strong
augmentation harms the batch normalisation statistics. We design a new batch
normalisation, namely distribution-specific batch normalisation (DSBN) to
address this problem and demonstrate the importance of strong augmentation for
semantic segmentation. Moreover, we design a self correction loss which is
effective in noise resistance. We conduct a series of ablation studies to show
the effectiveness of each component. Our method achieves state-of-the-art
results in the semi-supervised settings on the Cityscapes and Pascal VOC
datasets.
