Semi-supervised semantic segmentation needs strong, varied perturbations

Understand

Consistency regularization describes a class of approaches that have yielded ground breaking results in semi-supervised classification problems.

• Prior work has established the cluster assumption - under which the data distribution consists of uniform class clusters of samples separated by low density regions - as important to its success.
• We analyze the problem of semantic segmentation and find that its' distribution does not exhibit low density regions separating classes and offer this as an explanation for why semi-supervised segmentation is a challenging problem, with only a few reports of success.
• We then identify choice of augmentation as key to obtaining reliable performance without such low-density regions.