  Semi-supervised learning an attractive technique in practical deployments of
deep models since it relaxes the dependence on labeled data. It is especially
important in the scope of dense prediction because pixel-level annotation
requires significant effort. This paper considers semi-supervised algorithms
that enforce consistent predictions over perturbed unlabeled inputs. We study
the advantages of perturbing only one of the two model instances and preventing
the backward pass through the unperturbed instance. We also propose a
competitive perturbation model as a composition of geometric warp and
photometric jittering. We experiment with efficient models due to their
importance for real-time and low-power applications. Our experiments show clear
advantages of (1) one-way consistency, (2) perturbing only the student branch,
and (3) strong photometric and geometric perturbations. Our perturbation model
outperforms recent work and most of the contribution comes from photometric
component. Experiments with additional data from the large coarsely annotated
subset of Cityscapes suggest that semi-supervised training can outperform
supervised training with the coarse labels.
