提出新方法解决无监督域适应模型选择难题，无需目标标签也能准确评估性能。
Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation
- 将适配后的特征嵌入验证过程，实现无偏的目标风险估计
- 通过控制变量技术降低估计方差，提升稳定性
- 理论与实证双重验证，适合研究者对比域适应算法
深度无监督域适应（Deep UDA）方法能利用源域丰富的标注数据提升目标域（无标签）的性能。然而，由于缺乏准确且标准化的模型选择方法，算法比较变得复杂，阻碍了领域进展。现有方法或高度偏差、受限、不稳定，甚至存在争议（需目标域标签）。为此，本文提出深度嵌入验证（Deep Embedded Validation, DEV），将适配后的特征表示嵌入验证流程，以获得具有有界方差的无偏目标风险估计。进一步采用控制变量技术降低方差。该方法在理论和实证上均被证明有效。
原文摘要 · Abstract (English)
Deep unsupervised domain adaptation (Deep UDA) methods successfully leverage rich labeled data in a source domain to boost the performance on related but unlabeled data in a target domain. However, algorithm comparison is cumbersome in Deep UDA due to the absence of accurate and standardized model selection method, posing an obstacle to further advances in the field. Existing model selection methods for Deep UDA are either highly biased, restricted, unstable, or even controversial (requiring labeled target data). To this end, we propose \textit{Deep Embedded Validation} (\textbf{DEV}), which embeds adapted feature representation into the validation procedure to obtain unbiased estimation of the target risk with bounded variance. The variance is further reduced by the technique of control variate. The efficacy of the method has been justified both theoretically and empirically.
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