  We propose Dense FixMatch, a simple method for online semi-supervised
learning of dense and structured prediction tasks combining pseudo-labeling and
consistency regularization via strong data augmentation. We enable the
application of FixMatch in semi-supervised learning problems beyond image
classification by adding a matching operation on the pseudo-labels. This allows
us to still use the full strength of data augmentation pipelines, including
geometric transformations. We evaluate it on semi-supervised semantic
segmentation on Cityscapes and Pascal VOC with different percentages of labeled
data and ablate design choices and hyper-parameters. Dense FixMatch
significantly improves results compared to supervised learning using only
labeled data, approaching its performance with 1/4 of the labeled samples.
