  Minimization of distribution matching losses is a principled approach to
domain adaptation in the context of image classification. However, it is
largely overlooked in adapting segmentation networks, which is currently
dominated by adversarial models. We propose a class of loss functions, which
encourage direct kernel density matching in the network-output space, up to
some geometric transformations computed from unlabeled inputs. Rather than
using an intermediate domain discriminator, our direct approach unifies
distribution matching and segmentation in a single loss. Therefore, it
simplifies segmentation adaptation by avoiding extra adversarial steps, while
improving both the quality, stability and efficiency of training. We juxtapose
our approach to state-of-the-art segmentation adaptation via adversarial
training in the network-output space. In the challenging task of adapting brain
segmentation across different magnetic resonance images (MRI) modalities, our
approach achieves significantly better results both in terms of accuracy and
stability.
