  Consistency regularization describes a class of approaches that have yielded
state-of-the-art results for semi-supervised classification. While
semi-supervised semantic segmentation proved to be more challenging, a number
of successful approaches have been recently proposed. Recent work explored the
challenges involved in using consistency regularization for segmentation
problems. In their self-supervised work Chen et al. found that colour
augmentation prevents a classification network from using image colour
statistics as a short-cut for self-supervised learning via instance
discrimination. Drawing inspiration from this we find that a similar problem
impedes semi-supervised semantic segmentation and offer colour augmentation as
a solution, improving semi-supervised semantic segmentation performance on
challenging photographic imagery.
