Research map / Label-efficient and robust learning / Domain adaptation and generalization
Return of Frustratingly Easy Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| apply CORAL alignment to source features | linear SVM, SURF · Office-Caltech10 | accuracy: 37.8 -> 46.7 | Helped |
| apply CORAL on fine-tuned fc7 features | AlexNet + SVM · Office | accuracy: 65.5 -> 69.4 | Helped |
| apply CORAL | linear SVM, bag-of-words · Amazon reviews | accuracy: 76.7 -> 78.0 | Helped |
| apply CORAL | DECAF-fc7 + SVM · Cross-Dataset Testbed | accuracy: 38.5 -> 40.2 | Helped |
| use subspace methods (SA/GFK/TCA) instead of no adaptation | DECAF-fc7 + SVM · Cross-Dataset Testbed | accuracy: 38.5 -> 25.8 (SA) | Hurt |
| fine-tune source network before CORAL (ablation) | CORAL-fc7 · Office | accuracy: 66.9 -> 69.4 | Helped |
| use fc7 instead of fc6 features (ablation) | CORAL-FT · Office | accuracy: 68.5 -> 69.4 | Helped |
| fine-tune on source only without adaptation | AlexNet fc7 · Office | accuracy: 64.4 -> 65.5 | No clear effect |
| vary covariance regularization lambda (ablation) | CORAL · Office-Caltech10 | accuracy | No clear effect |
Closest papers by meaning
- Unsupervised Domain Adaptation by Matching Distributions Based on the Maximum Mean Discrepancy via Unilateral Transformations (AAAI 2019 · similarity 0.89)
- Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation (NeurIPS 2023 · similarity 0.89)
- Confusing Pair Correction Based on Category Prototype for Domain Adaptation under Noisy Environments (AAAI 2024 · similarity 0.88)
- Pseudo-Labeling for Unsupervised Domain Adaptation with Kernel GLMs (NeurIPS 2026 · similarity 0.88)
- Gradient-Based Sample Selection for Black-Box Universal Domain Adaptation (AAAI 2025 · similarity 0.87)
- Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective (CVPR 2024 · similarity 0.87)
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