Research map / Label-efficient and robust learning / Domain adaptation and generalization
Minimal-Entropy Correlation Alignment for Unsupervised Deep Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| replace Euclidean covariance alignment with geodesic log-Euclidean alignment (ablation) | Correlation alignment on SVHN→MNIST · SVHN→MNIST | target accuracy: about 5% | Helped |
| select λ by minimizing target entropy rather than tuning on target labels | MECA on SVHN→MNIST · SVHN→MNIST | target accuracy | Helped |
| use geodesic log-Euclidean alignment instead of Deep CORAL's Euclidean alignment (ablation) | Correlation alignment · SVHN→MNIST | normalized accuracy: +5% | Helped |
| use geodesic log-Euclidean alignment instead of Deep CORAL's Euclidean alignment (ablation) | Correlation alignment · NYUD RGB→depth | normalized accuracy: +2.1% | Helped |
| use geodesic log-Euclidean alignment instead of Deep CORAL's Euclidean alignment (ablation) | Correlation alignment · SYN DIGITS→SVHN | normalized accuracy: +0.5% | Helped |
| add correlation alignment to source-only training | MECA · SYN DIGITS→SVHN | normalized accuracy | Helped |
Closest papers by meaning
- Co-regularized Alignment for Unsupervised Domain Adaptation (NeurIPS 2018 · similarity 0.91)
- Unsupervised Domain Adaptation Using Feature-Whitening and Consensus Loss (CVPR 2019 · similarity 0.90)
- Universal Domain Adaptation through Self Supervision (NeurIPS 2020 · similarity 0.89)
- Exploiting Local Feature Patterns for Unsupervised Domain Adaptation (AAAI 2019 · similarity 0.89)
- Domain Adaptation with Adaptive $f$-Divergence: Tighter Variational Representation and Generalization Bounds (ICML 2026 · similarity 0.89)
- Tackling Dimensional Collapse toward Comprehensive Universal Domain Adaptation (ICML 2025 · similarity 0.87)
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