Bringing Equity to Classification: Domain Generalization for Domain-Linked Classes
Bibliographic record
Abstract
Domain generalization (DG) focuses on transferring domain-invariant knowledge from multiple source (training) domains to an a priori unseen target domain(s). This task implicitly requires that classes of interest are expressed in multiple sources (domain-shared) to break spurious domain-class correlations. However, real-world data scarcity challenges may often result in classes present in only a specific domain (domain-linked), which we show leads to extremely poor generalization. In this work, we introduce the domain-linked DG task to the community and develop a methodology to learn useful domain-invariant representations from domain-shared classes for domain-linked ones. Specifically, we propose FOND, a Fairness-inspired and cONtrastive learning objective for Domain-linked DG. Rigorous and reproducible experimental results communicate that FOND accomplishes state-of-the-art improvements for domain-linked classes, given a sufficient number of domain-shared classes and with minimal performance trade-offs. Complementary to these contributions, we theoretically analyze this task and provide practical insights for domain-linked class generalizability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".