Abstract / truncated to 115 words (read the full abstract)

The recent success of deep learning is conditioned on the availability of large annotated datasets for supervised learning. Data annotation, however, is a laborious and a time-consuming task. When a model fully trained on an annotated source domain is applied to a target domain with different data distribution, a greatly diminished generalization performance can be observed due to domain shift. Unsupervised Domain Adaptation (UDA) aims to mitigate the impact of domain shift when the target domain is unannotated. The majority of UDA algorithms assume joint access between source and target data, which may violate data privacy restrictions in many real world applications. In this thesis I propose source-free UDA approaches that are well suited for ... toggle 6 keywords

domain adaptation – unsupervised domain adaptation – sequential domain adaptation – computer vision – medical image segmentation – sliced wasserstein distance

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