  In order to leverage and profit from unlabelled data, semi-supervised
frameworks for semantic segmentation based on consistency training have been
proven to be powerful tools to significantly improve the performance of purely
supervised segmentation learning. However, the consensus principle behind
consistency training has at least one drawback, which we identify in this
paper: imbalanced label distributions within the data. To overcome the
limitations of standard consistency training, we propose a novel
semi-supervised framework for semantic segmentation, introducing additional
losses based on prior knowledge. Specifically, we propose a light-weight
architecture consisting of a shared encoder and a main decoder, which is
trained in a supervised manner. An auxiliary decoder is added as additional
branch in order to make use of unlabelled data based on consensus training, and
we add additional constraints derived from prior information on the class
distribution and on auto-encoder regularisation. Experiments performed on our
"concrete aggregate dataset" presented in this paper demonstrate the
effectiveness of the proposed approach, outperforming the segmentation results
achieved by purely supervised segmentation and standard consistency training.
