Bridging domain gaps in CNNs: a comprehensive approach with adaptation and randomization strategies
Bibliographic record
Abstract
Metric-based methods are promising approaches to domain adaptation, aiming to align the marginal distributions of different domains with similar conditional distributions. In traditional approaches, the metric function is manually designed to measure the distance across domains. Adversarial methods can be considered an automatic learning approach to the metric function. Instead of relying on the quality of the metric function, we outline a generalized framework for domain randomization, which first introduces moderate perturbations as a form of randomness and then combines the advantages of metric-based domain adaptation with domain randomization. We propose a novel approach leveraging simulation environments to generate extensive, annotated data for diverse scenarios. Our focus is on simulation-to-real transfer for semantic segmentation tasks, acknowledging CNN’s texture bias. We introduce a domain randomization technique that limits meaningful texture information and devise a mapping function for image transformation. The proposed approach emphasizes geometry consistency and style transfer, providing a practical solution for efficient simulation to real-world transfer. The experiments are conducted on the domain generalization task from GTA to Cityscapes and BDD, and evaluated on the semantic segmentation performance. Our method achieves superior results in most segmentation classes compared with the benchmark models by 5.2 to 10 in terms of mIoU. Remarkably, our generalization strategy does not require access the target domain data at training time.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".