论文标题
连续索引域的适应
Continuously Indexed Domain Adaptation
论文作者
论文摘要
现有的域适应性重点是用分类指数（例如，数据集A和B之间）在域之间传输知识。但是，许多任务涉及连续索引域。例如，在医疗应用中，通常需要在不同年龄的患者中转移疾病分析和预测，而年龄充当连续域指数。这些任务对于先前的域适应方法而言是具有挑战性的，因为它们忽略了域之间的基本关系。在本文中，我们提出了第一种连续索引域适应性的方法。我们的方法将传统的对抗适应与新颖的歧视器结合起来，该歧视器模拟编码条件的域指数分布。我们的理论分析证明了利用域索引在连续的域范围内生成不变特征的价值。我们的经验结果表明，我们的方法优于合成和现实医学数据集上的最新域适应方法。
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.
