论文标题
多源域适应中的源选择课程经理
Curriculum Manager for Source Selection in Multi-Source Domain Adaptation
论文作者
论文摘要
多源无监督域的适应性的性能显着取决于从标记的源域样品转移的有效性。在本文中，我们提出了一种对抗性药物，该代理学习了用于源样本的动态课程，称为源选择课程管理器（CMSS）。独立网络模块课程管理器在培训期间不断更新课程，并迭代地学习哪些域或样本最适合与目标保持一致。这背后的直觉是迫使课程管理器不断重新衡量潜在领域随着时间的流逝的转移性，以使域歧视者的错误率提高。 CMS不需要了解域标签的任何知识，但是它通过大量边距在四个众所周知的基准上胜过其他方法。我们还提供了可解释的结果，以阐明所提出的方法。
The performance of Multi-Source Unsupervised Domain Adaptation depends significantly on the effectiveness of transfer from labeled source domain samples. In this paper, we proposed an adversarial agent that learns a dynamic curriculum for source samples, called Curriculum Manager for Source Selection (CMSS). The Curriculum Manager, an independent network module, constantly updates the curriculum during training, and iteratively learns which domains or samples are best suited for aligning to the target. The intuition behind this is to force the Curriculum Manager to constantly re-measure the transferability of latent domains over time to adversarially raise the error rate of the domain discriminator. CMSS does not require any knowledge of the domain labels, yet it outperforms other methods on four well-known benchmarks by significant margins. We also provide interpretable results that shed light on the proposed method.
