On Direct Distribution Matching for Adapting Segmentation Networks

Georg Pichler, Jose Dolz, Ismail Ben Ayed, Pablo Piantanida

• Keywords: domain adaptation, unsupervised domain adaptation, semantic segmentation, direct distribution matching
• Track: full conference paper
• Abstract: 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 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 imaging (MRI) modalities, our approach achieves significantly better results both in terms of accuracy and stability.
• Paper Type: methodological development
• TL;DR: We propose a simple approach allowing for efficient domain adaptation in semantic segmentation problems if source and target domain data are aligned.
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