mlsanityData to 7 Oct 2026

Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation

arXiv 2606.04665 ↗First posted 3 Jun 2026cs.LGcited by 3

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Abstract (arXiv)

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 textitDeep Embedded Validation (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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 18 of its 37 references that are in the map. First posted on arXiv 3 Jun 2026.

Builds on

18 of 37 references.

References · 18

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6 more
  1. 2018Open Set Domain Adaptation by Backpropagationcited by 150
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 3.

3 papers.

  1. 2026Training-Free Model Selection and Domain-Aware Score Calibration for First-Shot Anomalous Sound Detectioncited by 0
  2. 2026Towards Practical Algorithm Selection for Unsupervised Domain Adaptation in Medical Imagingcited by 0
  3. 2026How Far from Clinical Deployment? Evaluating the Complete Unsupervised Domain Adaptation Pipeline in Medical Imagingcited by 0