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Unsupervised Domain Adaptation: A Reality Check

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arxiv 2111.15672 v1 pith:W7V5CTGQ submitted 2021-11-30 cs.CV

Kevin Musgrave , Serge Belongie , Ser-Nam Lim This is my paper · ORCID
classification cs.CV
keywords methodsaccuracyalgorithmsvalidationdomainadaptationunsupervisedabsence
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Interest in unsupervised domain adaptation (UDA) has surged in recent years, resulting in a plethora of new algorithms. However, as is often the case in fast-moving fields, baseline algorithms are not tested to the extent that they should be. Furthermore, little attention has been paid to validation methods, i.e. the methods for estimating the accuracy of a model in the absence of target domain labels. This is despite the fact that validation methods are a crucial component of any UDA train/val pipeline. In this paper, we show via large-scale experimentation that 1) in the oracle setting, the difference in accuracy between UDA algorithms is smaller than previously thought, 2) state-of-the-art validation methods are not well-correlated with accuracy, and 3) differences between UDA algorithms are dwarfed by the drop in accuracy caused by validation methods.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Consensus-Driven Active Model Selection

    cs.LG 2025-07 conditional novelty 7.0 of 10

    CODA uses consensus-based priors and Bayesian updating to select the best candidate model with far fewer labels than prior active model selection methods, beating them on 18 of 26 benchmark tasks.

  2. Towards Practical Algorithm Selection for Unsupervised Domain Adaptation in Medical Imaging

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Two-level validator-and-algorithm agreement selects a single UDA checkpoint without target labels and halves the gap to the labeled oracle versus the best individual validator on seven medical transfers.

  3. Does Unsupervised Domain Adaptation Improve the Robustness of Amortized Bayesian Inference? A Systematic Evaluation

    stat.ML 2025-02 conditional novelty 6.0 of 10

    Aligning simulated and observed summaries improves neural Bayesian inference under likelihood misspecification but degrades it under prior misspecification.

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