Pseudo Labels for Unsupervised Domain Adaptation: A Review
Open accessAbstract
Conventional machine learning relies on two presumptions: (1) the training and testing datasets follow the same independent distribution, and (2) an adequate quantity of samples is essential for achieving optimal model performance during training. Nevertheless, meeting these two assumptions can be challenging in real-world scenarios. Domain adaptation (DA) is a subfield of transfer learning that focuses on reducing the distribution difference between the source domain (Ds) and target domain (Dt) and subsequently applying the knowledge gained from the Ds task to the Dt task. The majority of current DA methods aim to achieve domain invariance by aligning the marginal probability distributions of the Ds. and Dt. Recent studies have pointed out that aligning marginal probability distributions alone is not sufficient and that alignment of conditional probability distributions is equally important for knowledge migration. Nonetheless, unsupervised DA presents a more significant difficulty in aligning the conditional probability distributions because of the unavailability of labels for the Dt. In response to this issue, there have been several proposed methods by researchers, including pseudo-labeling, which offer novel solutions to tackle the problem. In this paper, we systematically analyze various pseudo-labeling algorithms and their applications in unsupervised DA. First , we summarize the pseudo-label generation methods based on the single and multiple classifiers and actions taken to deal with the problem of imbalanced samples. Second, we investigate the application of pseudo-labeling in category feature alignment and improving feature discrimination. Finally, we point out the challenges and trends of pseudo-labeling algorithms. As far as we know, this article is the initial review of pseudo-labeling techniques for unsupervised DA.
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The absence of strict constraints in direct regression as pseudo-label filtering criteria hinders the application of self-training techniques during the cross-domain adaptation process.
“Moreover, the absence of strict constraints in direct regression as pseudo-label filtering criteria hinders the application of self-training techniques during the cross-domain adaptation process [20].”
From Pose Estimation for Cross-Domain Non-Cooperative Spacecraft Based on Spatial-Aware Keypoints Regression (2024) · cites this paper as [20]
Section 2.2 describes self-training with confident pseudo-label predictions, and sections 3.2 and 4.1 discuss thresholds and pseudo-label selection. None of the passages found addresses direct regression as a filtering criterion or says that a lack of strict constraints hinders self-training in cross-domain adaptation (searched: direct regression, strict constraints, filtering criteria). (LLM, conf=0.73)
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- Authors
- Li, Y., Guo, L., & Ge, Y.
- Affiliations
- North China University of Technology (CN)
- Corresponding author
- Li, Yundong
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2023-08-03
- Volume
- 12
- Issue
- 15
- Pages
- 3325
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W4385543003
- Cited by
- 51
- References
- 68
- Citations by year
- 2026: 11 · 2025: 25 · 2024: 14 · 2023: 1
- FWCI
- 4.51
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (62071006)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Keywords
- unsupervised domain adaptation, pseudo-labeling, domain invariance, transfer learning, limited labeled data, feature discrimination, marginal distribution alignment
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