Deep learning models can achieve high accuracy when trained on large amounts
of labeled data. However, real-world scenarios often involve several
challenges: Training data may become available in installments, may originate
from multiple different domains, and may not contain labels for training.
Certain settings, for instance medical applications, often involve further
restrictions that prohibit retention of previously seen data due to privacy
regulations. In this work, to address such challenges, we study unsupervised
segmentation in continual learning scenarios that involve domain shift. To that
end, we introduce GarDA (Generative Appearance Replay for continual Domain
Adaptation), a generative-replay based approach that can adapt a segmentation
model sequentially to new domains with unlabeled data. In contrast to
single-step unsupervised domain adaptation (UDA), continual adaptation to a
sequence of domains enables leveraging and consolidation of information from
multiple domains. Unlike previous approaches in incremental UDA, our method
does not require access to previously seen data, making it applicable in many
practical scenarios. We evaluate GarDA on two datasets with different organs
and modalities, where it substantially outperforms existing techniques.

本文提出了一种基于生成式回放的无监督分割方法 GarDA（用于连续域自适应），通过不断适应一系列新的未标记数据（无需访问旧数据），使得分割模型可以对多个不同的域进行适应，并表现出比现有技术有显著的优势。