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Comprehensive collection of domain adaptation research papers
Passed 18 of 23 test cases with the skill, and 16 of 23 without it.
2 fixed16 passed either way5 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 2 test cases the skill fixed. Same model, same prompt, with and without the skill.
What is the key practical constraint difference between Source-free Domain Adaptation and Test-time Adaptation? I assumed Test-time Adaptation requires offline target dataset training without source data, whereas Source-free DA adapts dynamically on individual test samples during inference.
| Checked | Without skill | With skill |
|---|---|---|
| Source-free DA is defined by the absence of source data during adaptation. | Missed | Passed |
| Test-time adaptation is defined as adapting the model at inference time. | Passed | Passed |
The instructions your model reads when the skill loads.
Domain adaptation addresses the problem of training models on one data distribution (source domain) and deploying them on a different distribution (target domain). This curated collection covers the full spectrum — from unsupervised domain adaptation (UDA) and domain generalization to partial, open-set, and source-free adaptation. Organized by methodology and application area with regularly updated paper lists.
Domain Adaptation
├── Unsupervised DA (UDA)
│ ├── Discrepancy-based (MMD, CORAL, CDD)
│ ├── Adversarial-based (DANN, ADDA, CDAN)
│ ├── Reconstruction-based (DRCN, DSN)
│ └── Self-training (SHOT, CBST)
├── Semi-supervised DA
├── Source-free DA (no source data at adaptation time)
├── Partial DA (target has subset of source classes)
├── Open-set DA (target has unknown classes)
├── Universal DA (no prior on label set relationship)
├── Multi-source DA
├── Domain Generalization (no target data at all)
└── Test-time Adaptation (adapt at inference)
| Method | Year | Approach | Key Idea |
|---|---|---|---|
| TCA | 2011 | Kernel | Transfer Component Analysis |
| GFK | 2012 | Subspace | Geodesic Flow Kernel |
| SA | 2013 | Subspace | Subspace Alignment |
| DAN | 2015 | MMD | Deep Adaptation Networks |
| DANN | 2016 | Adversarial | Domain-Adversarial Neural Networks |
| ADDA | 2017 | Adversarial | Adversarial Discriminative DA |
| CORAL | 2016 | Statistics | Correlation Alignment |
test cases passed, vs 16 / 23 without
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