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.

翻译：深度学习模型在大量标注数据上训练时能够达到高精度。然而，实际场景往往面临多个挑战：训练数据可能分批获取、可能来自多个不同域、且可能不含训练标签。某些场景（如医疗应用）常涉及额外限制，因隐私规定禁止保留先前所见数据。为应对这些挑战，本文研究了涉及域漂移的持续学习场景中的无监督分割问题。为此，我们提出GarDA（持续域适应的生成式外观回放），一种基于生成式回放的方法，能够使分割模型利用无标签数据依次适应新域。与单步无监督域适应（UDA）不同，持续适应多域序列能够利用并整合来自多个域的信息。与增量UDA的先前方法不同，我们的方法无需访问先前数据，因此适用于多种实际场景。我们在两个不同器官和模态的数据集上评估GarDA，其显著优于现有技术。