Bibliographic record
Abstract
Unsupervised domain adaptation is the problem of transferring extracted knowledge from a labeled source domain to an unlabeled target domain. To achieve discriminative domain adaptation recent studies take advantage of target sample pseudo-labels to impose class-aware distribution alignment across the source and target domains. Still, they have some shortcomings such as making decisions based on inaccurate pseudo-labeled samples that mislead the adaptation process. In this paper, we propose a progressive deep feature alignment, called Norma, to tackle class-aware unsupervised domain adaptation for image classification by enforcing inter-class compactness and intra-class discrepancy through a hybrid learning process. To this end, Norma's optimization process is defined based on a novel triplet loss which not only addresses soft prototype alignment but also pushes away multiple negative centroids. Also, to extract maximum discriminative domain knowledge per iteration, we propose a joint positive and negative learning procedure along with an uncertainty-guided progressive pseudo-labeling on the basis of prototype-based clustering and conditional probability. Our experimental results on several benchmarks demonstrate that Norma outperforms the state-of-the-art methods.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.015 |
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".