f-Domain-Adversarial Learning: Theory and Algorithms for Unsupervised Domain Adaptation with Neural Networks
Bibliographic record
Abstract
The problem of unsupervised domain adaptation arises in a variety of practical applications where the distribution of the training samples differs from those used at test time. The existing theory of domain adaptation derived generalization bounds based on divergence measures that are hard to optimize in practice. This has led to a large disconnect between theory and state-of-the-art methods. In this paper, we propose a novel domain-adversarial framework that introduces new theory for domain adaptation and leads to practical learning algorithms with neural networks. In particular, we derive a novel generalization bound that utilizes a new measure of discrepancy between distributions based on a variational characterization of f-divergences. We show that our bound recovers the theoretical results from Ben-David et al. (2010a) as a special case with a particular choice of divergence, and also supports divergences typically used in practice. We derive a general algorithm for domain-adversarial learning for the complete family of f-divergences. We provide empirical results for several f-divergences and show that some, not considered previously in domain-adversarial learning, achieve state-of-the-art results in practice. We provide empirical insights into how choosing a particular divergence affects the transfer performance on real-world datasets. By further recognizing the optimization problem as a Stackelberg game, we utilize the latest optimizers from the game optimization literature, achieving additional performance boosts in our training algorithm. We show that our f-domain adversarial framework achieves state-of-the-art results on the challenging Office-31 and Office-Home datasets without extra hyperparameters.
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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 | 0.000 | 0.000 |
| 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".