domain-adaptation-papers-guide
Comprehensive collection of domain adaptation research papers
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill domain-adaptation-papers-guide --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Domain Adaptation Papers Guide ## Overview 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. ## Taxonomy of Methods ``` 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) ``` ## Key Methods by Era ### Classical Methods | Method | Year | Approach | Key Idea | |--------|------|----------|----------| | **TCA** | 2011 | Kernel | Transfer C
- Overview
- Taxonomy of Methods
- Key Methods by Era
- Classical Methods
- Modern Methods
- Paper Tracking
- Benchmark Datasets
- Application Areas
- Reading Roadmap
- Use Cases
- References
What does the domain-adaptation-papers-guide skill do?
Comprehensive collection of domain adaptation research papers
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill domain-adaptation-papers-guide --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.