Research map / Label-efficient and robust learning / Domain adaptation and generalization
COT: Unsupervised Domain Adaptation With Clustering and Optimal Transport
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use clustering-level rather than sample-level optimal transport | DANN + MMI · Office-31 | accuracy (%) | Helped |
| Use clustering-level rather than sample-level optimal transport | DANN + MMI · CI30, CI50, and CI70 | mean accuracy | Helped |
| Increase α1 and α2 to 0.4, 1.0, or 2.0 from 0.2 (ablation) | COT · Office-31 | mean accuracy: 91.0; 90.6; 89.4; 87.7 | Hurt |
| Set α1 and α2 to 0.1 instead of 0.2 (ablation) | COT · Office-31 | mean accuracy: 91.0; 90.8 | No clear effect |
| Vary target sub-domains per class Q from 2 to 1, 3, or 5 (ablation) | COT · Office-31 | mean accuracy: 91.0; 90.7; 90.8; 90.4 | No clear effect |
Closest papers by meaning
- Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering (CVPR 2020 · similarity 0.90)
- Unified Optimal Transport Framework for Universal Domain Adaptation (NeurIPS 2022 · similarity 0.89)
- Enhanced Transport Distance for Unsupervised Domain Adaptation (CVPR 2020 · similarity 0.89)
- Prototypical Partial Optimal Transport for Universal Domain Adaptation (AAAI 2023 · similarity 0.89)
- Theoretical Performance Guarantees for Partial Domain Adaptation via Partial Optimal Transport (ICML 2025 · similarity 0.88)
- Can We Evaluate Domain Adaptation Models Without Target-Domain Labels? (ICLR 2024 · similarity 0.87)
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