SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation
Bibliographic record
Abstract
Unsupervised domain adaptation (UDA) aims at knowledge transfer from a labeled source domain to an unlabeled target domain. Most UDA techniques achieve this by reducing feature discrepancies between the two domains to learn domain-invariant feature representations. In this paper, we enhance this approach by proposing a simple yet powerful probabilistic framework (SimProF) for UDA to minimize the domain gap between the two domains. SimProF estimates the feature space distribution for each class and generates contrastive pairs by leveraging the shared categories between the source and target domains. The concept behind SimProF is inspired by the observation that normalized features in contrastive learning tend to follow a mixture of von Mises-Fisher (vMF) distributions on the unit sphere. This characteristic allows for the generation of an infinite number of contrastive pairs and facilitates an efficient optimization method using a closed-form expression for the expected contrastive loss. As a result, target semantics can be effectively used to augment source features. To implement this, we create vMF distributions based on the inter-domain feature mean difference for each class. Notably, we derive and minimize an upper bound of the expected loss, which is implicitly achieved through an estimated supervised contrastive learning loss applied to the augmented source distribution. Comprehensive experiments on cross-domain benchmarks confirm the efficacy of the 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".