Research map / Label-efficient and robust learning / Domain adaptation and generalization
On Learning Invariant Representations for Domain Adaptation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Apply the invariant transformation g to the counterexample inputs | Interval-classification counterexample · Counterexample, source and target | joint error: 0 -> 1 | Hurt |
| Over-train the feature transformation and discriminator | DANN · USPS → MNIST, USPS → SVHN, SVHN → MNIST, SVHN → USPS | target test accuracy | Hurt |
| Train DANN through the early adaptation phase | DANN · Digit classification domain adaptation | target test accuracy | Helped |
| Increase label-distribution discrepancy between source and target | DANN · Unbalanced MNIST → USPS | target test accuracy during adaptation | Hurt |
| Match marginal distributions and reduce source error without accounting for conditional shift | Invariant-representation domain adaptation · General domain adaptation | target error | Mixed |
| Align representations while source and target marginal label distributions differ | Invariant-representation domain adaptation · General domain adaptation | joint error | Hurt |
Closest papers by meaning
- Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? (NeurIPS 2021 · similarity 0.94)
- FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation (CVPR 2021 · similarity 0.90)
- Domain Invariant Representation Learning with Domain Density Transformations (NeurIPS 2021 · similarity 0.90)
- Distribution-Informed Neural Networks for Domain Adaptation Regression (NeurIPS 2022 · similarity 0.90)
- A General Representation-Based Approach to Multi-Source Domain Adaptation (ICML 2025 · similarity 0.89)
- Enhancing Domain Adaptation through Prompt Gradient Alignment (NeurIPS 2024 · similarity 0.88)
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