Cycle Consistency Based Pseudo Label and Fine Alignment for Unsupervised Domain Adaptation
Bibliographic record
Abstract
Unsupervised Domain Adaptation (UDA) aims to transfer knowledge from a well-labeled source domain to an unlabeled target domain with a correlative distribution. Numerous existing approaches process this hard nut by directly matching the marginal distribution between two domains, which confront the obstacle of rough alignment and blurred decision boundary. Recent advances in UDA introduce target pseudo-label and subdomain adaptation to reduce misalignment and distribution discrepancy. Whereas, they frequently ignore that the production of target pseudo-label is so dependent on the source-trained classifier, which without reasonable restriction to discriminate generated pseudo-label is whether confident. Meanwhile, many methods in the subdomain alignment metric ignore exploring the potential distribution discrepancy between same-class samples of the intra-domain. To address these two issues simultaneously, this paper proposes a Cycle Consistency based Pseudo Label and Fine Alignment (CCPLFA) approach for UDA. In particular, firstly, a novel cycle-consistency based pseudo label module is designed, which is a simple yet effective way to alleviate the noise of pseudo labels and improve their semantic correctness. Secondly, we develop a Fine-Alignment distribution matching metric. Which can maximize the feature distribution density of intra-class cross-domains and not overlook the distribution structure of the global aspect. Comprehensive experiment results on four benchmarks demonstrate the capability of plug and play and the well generalization performance of our proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".