Existing domain adaptation focuses on transferring knowledge between domains with categorical indices (e.g., between datasets A and B). However, many tasks involve continuously indexed domains. For example, in medical applications, one often needs to transfer disease analysis and prediction across patients of different ages, where age acts as a continuous domain index. Such tasks are challenging for prior domain adaptation methods since they ignore the underlying relation among domains. In this paper, we propose the first method for continuously indexed domain adaptation. Our approach combines traditional adversarial adaptation with a novel discriminator that models the encoding-conditioned domain index distribution. Our theoretical analysis demonstrates the value of leveraging the domain index to generate invariant features across a continuous range of domains. Our empirical results show that our approach outperforms the state-of-the-art domain adaption methods on both synthetic and real-world medical datasets.

中文翻译：

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连续索引域适配

现有的领域适应侧重于在具有分类索引的领域之间（例如，在数据集 A 和 B 之间）转移知识。但是，许多任务涉及连续索引的域。例如，在医疗应用中，经常需要在不同年龄的患者之间传递疾病分析和预测，其中年龄充当连续域索引。这些任务对先前的域适应方法具有挑战性，因为它们忽略了域之间的潜在关系。在本文中，我们提出了连续索引域适应的第一种方法。我们的方法将传统的对抗性适应与一种新颖的鉴别器相结合，该鉴别器对编码条件下的域索引分布进行建模。我们的理论分析证明了利用域索引在连续域范围内生成不变特征的价值。我们的实证结果表明，我们的方法在合成和现实世界的医学数据集上都优于最先进的领域适应方法。