Research map / Label-efficient and robust learning / Domain adaptation and generalization
Source-free domain adaptation
Adapts models without source data using distribution estimation and domain-invariant parameters.
92 accepted papers at ICML, NeurIPS, ICLR, CVPR and AAAI (2016–2026) · an approach in Domain adaptation and generalization, keyword cluster “domain adaptation · unsupervised domain · uda”
State History Benchmarks Tried Checklist Preprints Who Export
Where it stands
Still moving The best reported result is still improving on 2 of 4 tracked benchmarks.
25 papers in 2023–24 and 23 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic held steady, from 7.3% to 7.2%.
Open questions the data points to
- Office-31, Avg. (accuracy (%)): best went from 90.3 (up to 2024) to 95.8 in 2025 — Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation (NeurIPS 2025 · 0 citations)
- Office-Home (Avg) (accuracy): best went from 74.8 (up to 2024) to 91.2 in 2025 — Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation (NeurIPS 2025 · 0 citations)
What looks settled
- Office-Home (accuracy): best up to 2024 78.42, best in 2025–26 74.1 from 2 papers, at most 0.2% better, if at all.
How this is decided
A benchmark is tracked when at least four papers of this approach report their own method on the same dataset and metric in at least two different years. Its record is the best number reported up to 2024 compared with the best reported in 2025–26: still improving means at least 1% better, flat means at most 0.2% better with at least two papers from 2025–26 trying. Numbers on a different scale from the rest (a fraction among percentages) and far outliers are left out. Results come from different papers with different setups, so they are signals, not head-to-head comparisons. The share of papers is counted at ICML, NeurIPS, ICLR, CVPR and AAAI.
How it got here
Most cited paper of each year
- 2016 · Return of Frustratingly Easy Domain Adaptation (AAAI 2016 · 1,920 citations)
- 2017 · Fast Generalized Distillation for Semi-Supervised Domain Adaptation (AAAI 2017 · 57 citations)
- 2018 · Wasserstein Distance Guided Representation Learning for Domain Adaptation (AAAI 2018 · 752 citations)
- 2019 · Separate to Adapt: Open Set Domain Adaptation via Progressive Separation (CVPR 2019 · 308 citations)
- 2020 · Universal Source-Free Domain Adaptation (CVPR 2020 · 240 citations)
- 2021 · Domain Adaptation With Auxiliary Target Domain-Oriented Classifier (CVPR 2021 · 172 citations)
- 2022 · Source-Free Domain Adaptation via Distribution Estimation (CVPR 2022 · 156 citations)
- 2023 · C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation (CVPR 2023 · 89 citations)
- 2024 · Source-Free Domain Adaptation with Frozen Multimodal Foundation Model (CVPR 2024 · 50 citations)
- 2025 · Collaborative Semantic Consistency Alignment for Blended-Target Domain Adaptation (AAAI 2025 · 4 citations)
- 2026 · Rethinking the Flow-based Gradual Domain Adaptation: A Semi-Dual Optimal Transport Perspective (ICML 2026 · 0 citations)
Earliest papers here
- Return of Frustratingly Easy Domain Adaptation (AAAI 2016 · 1,920 citations)
- Domain Separation Networks (NeurIPS 2016 · 1,102 citations)
- Domain Adaptation with Conditional Transferable Components (ICML 2016 · 208 citations)
Benchmark records
The best number each year that beat every earlier one, among papers of this approach reporting their own method. Numbers are extracted by AI from each paper's text; check the paper before citing.
Office-31 · accuracy (higher is better)
7 papers, 2020–2025 · one report since 2024, not better
| Year | Best so far | Set by |
|---|---|---|
| 2020 | 84.2 | Multi-Source Distilling Domain Adaptation (AAAI 2020 · 218 citations) |
| 2021 | 84.7 | Partial Feature Selection and Alignment for Multi-Source Domain Adaptation (CVPR 2021 · 31 citations) |
| 2022 | 89.9 | Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation (AAAI 2022 · 58 citations) |
| 2023 | 91.9 | Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation (AAAI 2023 · 21 citations) |
Office-Home · accuracy (higher is better)
6 papers, 2022–2025 · flat since 2024
| Year | Best so far | Set by |
|---|---|---|
| 2022 | 72.2 | Denoised Maximum Classifier Discrepancy for Source-Free Unsupervised Domain Adaptation (AAAI 2022 · 58 citations) |
| 2023 | 76.3 | Discriminability and Transferability Estimation: A Bayesian Source Importance Estimation Approach for Multi-Source-Free Domain Adaptation (AAAI 2023 · 21 citations) |
| 2024 | 78.42 | UFDA: Universal Federated Domain Adaptation with Practical Assumptions (AAAI 2024 · 15 citations) |
Office-31, Avg. · accuracy (%) (higher is better)
5 papers, 2020–2025 · still improving
| Year | Best so far | Set by |
|---|---|---|
| 2020 | 89.5 | Your Classifier can Secretly Suffice Multi-Source Domain Adaptation (NeurIPS 2020 · 21 citations) |
| 2022 | 90.3 | Confidence Score for Source-Free Unsupervised Domain Adaptation (ICML 2022 · 21 citations) |
| 2025 | 95.8 | Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation (NeurIPS 2025 · 0 citations) |
Office-Home (Avg) · accuracy (higher is better)
4 papers, 2020–2025 · still improving
| Year | Best so far | Set by |
|---|---|---|
| 2020 | 74.8 | Your Classifier can Secretly Suffice Multi-Source Domain Adaptation (NeurIPS 2020 · 21 citations) |
| 2025 | 91.2 | Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation (NeurIPS 2025 · 0 citations) |
What has been tried
78 papers here report 795 measured changes (a component added, removed or swapped, compared with a baseline): helped 78%, no clear effect 5%, hurt 13%, mixed 4%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Add LTSC task-specific ViL customization | Office-31, Office-Home, VisDA · average accuracy | 62.3 -> 81.4 | Helped | Source-Free Domain Adaptation with Frozen Multimodal Foundation Model (CVPR 2024 · 50 citations) |
| add UDN | Office-Home mean · accuracy | 6.13% | Helped | Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer (CVPR 2024 · 22 citations) |
| add a diversity loss to discriminability-only training | Synthetic inter-twinning moons · accuracy | 93.0 -> 95.3 | Helped | Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective (CVPR 2024 · 19 citations) |
| Add GCLD to PHL-only training | VisDA+ImageCLEF-DA · accuracy | 41.6 -> 66.6 | Helped | UFDA: Universal Federated Domain Adaptation with Practical Assumptions (AAAI 2024 · 15 citations) |
| Add neighbor center clustering to source cross-entropy training | D→A · HS | 38.70 -> 45.67 | Helped | Open-Set Graph Domain Adaptation via Separate Domain Alignment (AAAI 2024 · 14 citations) |
| Add style adaptation (SA) | DomainNet (Table 6 configuration; average) · accuracy | 67.6 -> 70.9 | Helped | Style Adaptation and Uncertainty Estimation for Multi-Source Blended-Target Domain Adaptation (NeurIPS 2024 · 6 citations) |
| Add the DSE module | Office-Home: Cl→Ar, Pr→Ar, and Rw→Ar · accuracy | Helped | Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation (CVPR 2024 · 5 citations) | |
| add SWD loss | Office-Home · accuracy (%) | 70.7 -> 73.0 | Helped | Collaborative Semantic Consistency Alignment for Blended-Target Domain Adaptation (AAAI 2025 · 4 citations) |
| Add IFA loss alone to SNC | VisDA-RSUT · Average accuracy | Hurt | SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation (ICLR 2024 · 3 citations) | |
| Add LSA and LCE to LIM | Office-Home and DomainNet126, six adaptation tasks · accuracy | 67.1 -> 73.8 | Helped | Revisiting Source-Free Domain Adaptation: Insights into Representativeness, Generalization, and Variety (CVPR 2025 · 2 citations) |
| add IPM projection | DomainNet MBDA · accuracy | 68.1 -> 70.1 | Helped | Invertible Projection and Conditional Alignment for Multi-Source Blended-Target Domain Adaptation (AAAI 2025 · 1 citation) |
| Add 2-stage learning | Multilingual Reuters Collection · HOS | 59.40 -> 65.39 | Helped | Open-Set Heterogeneous Domain Adaptation: Theoretical Analysis and Algorithm (AAAI 2025 · 1 citation) |
What reviewers will expect
Datasets and baselines named in the 13 papers of this approach from 2025–2026 that have a full-text summary. Leaving out the common ones is a frequent review question.
Datasets
- Office-Home · 8 of 13 papers
- Office-31 · 5 of 13 papers
- DomainNet · 4 of 13 papers
- VisDA · 4 of 13 papers
- ImageCLEF-DA · 3 of 13 papers
- DomainNet-126 · 3 of 13 papers
- ImageNet · 2 of 13 papers
- Caltech256 · 2 of 13 papers
Baselines
- SHOT · 3 of 13 papers
- AaD · 3 of 13 papers
- NRC · 3 of 13 papers
- AdaCon · 3 of 13 papers
- DANN · 2 of 13 papers
- CDAN · 2 of 13 papers
- MCDA · 2 of 13 papers
- AMDA · 2 of 13 papers
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 48 papers; from OpenAlex and the papers' affiliations).
- South China Normal University · 3 papers
- Shandong University · 2 papers
- The University of Hong Kong · 2 papers
- The University of Sydney · 2 papers
- The Hong Kong Polytechnic University · 2 papers
- Sun Yat-sen University · 2 papers
- Shanghai Jiao Tong University · 2 papers
- Google · 2 papers
Use it
Download BibTeX of 40 key papersOpen the topic in the interactive map
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Related approaches
Same keyword cluster
- Conditional distribution alignment (162 papers)
- Feature alignment for unsupervised adaptation (105 papers)
- Geometric alignment and synthetic-to-real adaptation (93 papers)
- Adversarial domain adaptation (95 papers)
- Statistical alignment and hypothesis testing (80 papers)
Elsewhere in the topic
- Cross-domain few-shot with transformers (116 papers)
- Optimization-based domain generalization (101 papers)
- Adaptation for semantic segmentation (81 papers)
- Statistical transfer learning (79 papers)
- Mixture and distillation for domain shift (78 papers)
- Foundation-model domain generalization (56 papers)
- Domain-generalized segmentation (56 papers)
- Cross-domain text and graph transfer (54 papers)
Benchmark numbers, ablations and baseline numbers are extracted by AI from each paper's full text and can be wrong; the paper is the authority. Data: results index of 2026-11-02, citation counts from OpenAlex (2026-10-09).