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RoDA: Robust Domain Alignment for Cross-Domain Retrieval Against Label Noise
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Proposes RoDA, a framework for cross-domain image retrieval under noisy labels that combines selective division with relabeling/adaptive weighting (SDAL) and accumulative class-center domain alignment (ACCA), evaluated on Office-31, Office-Home, and Adaptiope.
Problem
Cross-domain image retrieval where both query and gallery domains contain label noise, which degrades supervised retrieval and hinders domain-invariant feature learning.
Approach
Divides data into clean and noisy subsets via kNN label inconsistency, relabels noisy samples with soft assignments to clean-data class prototypes, weights relabeled samples adaptively, and aligns query/gallery domains through class centers accumulated batch-by-batch in a domain loss.
Key results
- On Office-Home at 20% noise, RoDA reaches 57.25 best mAP versus 54.90 for JoCoR and 55.04 for clean-label CE-C.
- At 80% noise on Office-Home, RoDA achieves 36.99 mAP, well above the next best GCE at 25.36.
- RoDA's ablation shows the full combination (SD+AL+ACCA) reaches 49.47 best mAP on Office-Home 20% noise, versus 32.99 for CE only.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add selective division, adaptive learning and ACCA (full RoDA) vs. CE only (ablation) | RoDA on Office-Home · Office-Home 20% noise, 12 tasks | mAP (Best): 49.47 vs 32.99 | Helped |
| Add ACCA on top of SD+AL (ablation) | RoDA on Office-Home · Office-Home 20% noise, 12 tasks | mAP (Best): 49.47 vs 47.97 | Helped |
| Add adaptive learning (relabeling and weighting) on top of SD (ablation) | RoDA on Office-Home · Office-Home 20% noise, 12 tasks | mAP (Best): 47.97 vs 44.39 | Helped |
| Add selective division alone over CE only (ablation) | RoDA on Office-Home · Office-Home 20% noise, 12 tasks | mAP (Best): 44.39 vs 32.99 | Helped |
| Full RoDA vs. baselines across noise ratios (Office-Home, Office-31, Adaptiope) | RoDA · Office-Home, Office-31, Adaptiope, 20%-80% noise | mAP@All (Best) | Helped |
| RoDA vs. Co-teaching at 20% noise on Adaptiope | RoDA · Adaptiope 20% noise | mAP (Best): 55.61 vs 58.16 | Hurt |
Surprising findings
- RoDA slightly underperforms Co-teaching at 20% noise on Adaptiope, which the authors attribute to category imbalance after division.
- RoDA at low noise surpasses cross entropy trained on clean labels (CE-C) in some settings, attributed to domain alignment.
Limitations
- Category imbalance after sample division can hurt performance on datasets with many categories (Adaptiope at 20% noise).
- Hyperparameters (beta, gamma, theta, alpha, lambda) are fixed and tuned per setup; sensitivity analysis is limited to beta and lambda.
Experimental setup
Datasets: Office-31 Office-Home Adaptiope
Benchmarks: Office-31 Office-Home Adaptiope
Metrics: mAP@All
Baselines: Cross Entropy Generalized Cross Entropy MAE Co-teaching JoCoR DivideMix NCE LongRemix DISC Robust-DivideMix
Builds on: DivideMix Generalized Cross Entropy Co-teaching JoCoR NCE
Closest papers by meaning
- DiDA: Disambiguated Domain Alignment for Cross-Domain Retrieval with Partial Labels (AAAI 2024 · similarity 0.91)
- Cross-Modal and Uni-Modal Soft-Label Alignment for Image-Text Retrieval (AAAI 2024 · similarity 0.88)
- Semantic Feature Learning for Universal Unsupervised Cross-Domain Retrieval (NeurIPS 2024 · similarity 0.88)
- COSDA: Counterfactual-based Susceptibility Risk Framework for Open-Set Domain Adaptation (ICML 2025 · similarity 0.86)
- Unsupervised Cross-Domain Image Retrieval via Prototypical Optimal Transport (AAAI 2024 · similarity 0.86)
- Retrieval Across Any Domains via Large-scale Pre-trained Model (ICML 2024 · similarity 0.86)
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