  In this paper, we present a novel cross-consistency based semi-supervised
approach for semantic segmentation. Consistency training has proven to be a
powerful semi-supervised learning framework for leveraging unlabeled data under
the cluster assumption, in which the decision boundary should lie in
low-density regions. In this work, we first observe that for semantic
segmentation, the low-density regions are more apparent within the hidden
representations than within the inputs. We thus propose cross-consistency
training, where an invariance of the predictions is enforced over different
perturbations applied to the outputs of the encoder. Concretely, a shared
encoder and a main decoder are trained in a supervised manner using the
available labeled examples. To leverage the unlabeled examples, we enforce a
consistency between the main decoder predictions and those of the auxiliary
decoders, taking as inputs different perturbed versions of the encoder's
output, and consequently, improving the encoder's representations. The proposed
method is simple and can easily be extended to use additional training signal,
such as image-level labels or pixel-level labels across different domains. We
perform an ablation study to tease apart the effectiveness of each component,
and conduct extensive experiments to demonstrate that our method achieves
state-of-the-art results in several datasets.
