Research map / Label-efficient and robust learning / Domain adaptation and generalization
Feature alignment for unsupervised adaptation
Progressively and dynamically aligns source and target features with analysis of limits.
105 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 Baselines Checklist Preprints Who Export
Where it stands
Still moving The best reported result is still improving on 2 of 6 tracked benchmarks.
27 papers in 2023–24 and 20 in 2025–26 (at ICML, NeurIPS, ICLR, CVPR and AAAI); its share of the topic shrank, from 7.9% to 6.3%.
Open questions the data points to
- Office-31 Avg (accuracy): best went from 90.8 (up to 2024) to 93.5 in 2025 — SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation (AAAI 2025 · 1 citation)
- Office-Home (Avg.) (accuracy): best went from 75.5 (up to 2024) to 76.7 in 2025 — SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation (AAAI 2025 · 1 citation)
- Papers report GLC on VisDA OSDA (H-score (%)) anywhere from 72.5 to 81.6: comparisons against it depend on whose number you take.
No longer reported by papers here: Office-31 W→A (accuracy (%), last in 2021); GTA5 → Cityscapes (mIoU, last in 2024); MNIST→USPS (accuracy (%), last in 2022).
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 · Domain-Constraint Transfer Coding for Imbalanced Unsupervised Domain Adaptation (AAAI 2016 · 20 citations)
- 2018 · Deep Cocktail Network: Multi-Source Unsupervised Domain Adaptation With Category Shift (CVPR 2018 · 416 citations)
- 2019 · Contrastive Adaptation Network for Unsupervised Domain Adaptation (CVPR 2019 · 967 citations)
- 2020 · Unsupervised Domain Adaptation via Structurally Regularized Deep Clustering (CVPR 2020 · 319 citations)
- 2021 · FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation (CVPR 2021 · 232 citations)
- 2022 · Category Contrast for Unsupervised Domain Adaptation in Visual Tasks (CVPR 2022 · 151 citations)
- 2023 · Guiding Pseudo-Labels With Uncertainty Estimation for Source-Free Unsupervised Domain Adaptation (CVPR 2023 · 138 citations)
- 2024 · Prompt-Based Distribution Alignment for Unsupervised Domain Adaptation (AAAI 2024 · 76 citations)
- 2025 · Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation (CVPR 2025 · 7 citations)
- 2026 · End-to-End Knowledge Distillation for Unsupervised Domain Adaptation with Large Vision-language Models (AAAI 2026 · 1 citation)
Earliest papers here
- Domain-Constraint Transfer Coding for Imbalanced Unsupervised Domain Adaptation (AAAI 2016 · 20 citations)
- Deep Cocktail Network: Multi-Source Unsupervised Domain Adaptation With Category Shift (CVPR 2018 · 416 citations)
- Unsupervised Domain Adaptation With Similarity Learning (CVPR 2018 · 313 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 Avg · accuracy (higher is better)
6 papers, 2019–2025 · still improving
| Year | Best so far | Set by |
|---|---|---|
| 2019 | 90.2 | Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources (AAAI 2019 · 398 citations) |
| 2020 | 90.8 | Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation (CVPR 2020 · 149 citations) |
| 2025 | 93.5 | SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation (AAAI 2025 · 1 citation) |
Office-Home (Avg.) · accuracy (higher is better)
6 papers, 2019–2025 · still improving
| Year | Best so far | Set by |
|---|---|---|
| 2019 | 74.1 | Aligning Domain-Specific Distribution and Classifier for Cross-Domain Classification from Multiple Sources (AAAI 2019 · 398 citations) |
| 2021 | 75.5 | Unsupervised Multi-Source Domain Adaptation Without Access to Source Data (CVPR 2021 · 151 citations) |
| 2025 | 76.7 | SimProF: A Simple Probabilistic Framework for Unsupervised Domain Adaptation (AAAI 2025 · 1 citation) |
Office-31 W→A · accuracy (%) (higher is better)
5 papers, 2019–2021 · not reported since 2021
| Year | Best so far | Set by |
|---|---|---|
| 2019 | 71.15 | Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation (ICML 2019) |
| 2021 | 72.5 | FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation (CVPR 2021 · 232 citations) |
GTA5 → Cityscapes · mIoU (higher is better)
4 papers, 2020–2024 · not reported since 2024
| Year | Best so far | Set by |
|---|---|---|
| 2020 | 43.6 | Stochastic Classifiers for Unsupervised Domain Adaptation (CVPR 2020 · 185 citations) |
| 2021 | 48.1 | Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data (NeurIPS 2021 · 95 citations) |
| 2022 | 49.2 | Category Contrast for Unsupervised Domain Adaptation in Visual Tasks (CVPR 2022 · 151 citations) |
MNIST→USPS · accuracy (%) (higher is better)
4 papers, 2019–2022 · not reported since 2022
| Year | Best so far | Set by |
|---|---|---|
| 2019 | 95 | Progressive Feature Alignment for Unsupervised Domain Adaptation (CVPR 2019 · 482 citations) |
| 2020 | 97.8 | Stochastic Classifiers for Unsupervised Domain Adaptation (CVPR 2020 · 185 citations) |
Office-31 · accuracy (%) (higher is better)
4 papers, 2021–2025 · one report since 2024, not better
| Year | Best so far | Set by |
|---|---|---|
| 2021 | 92.44 | Sample Selection for Universal Domain Adaptation (AAAI 2021 · 17 citations) |
What has been tried
95 papers here report 1,043 measured changes (a component added, removed or swapped, compared with a baseline): helped 78%, no clear effect 4%, hurt 15%, mixed 3%. Papers mostly publish changes that helped, so "helped" is over-represented.
Ablations in recent papers
| What was changed | Measured on | Result | Effect | Paper |
|---|---|---|---|---|
| Add the L_x source-classification loss | Office-Home · accuracy | +2.1 | Helped | Prompt-Based Distribution Alignment for Unsupervised Domain Adaptation (AAAI 2024 · 76 citations) |
| Add visual prompting only | Office-Home · accuracy | 75.3 -> 75.7 | Helped | Domain-Agnostic Mutual Prompting for Unsupervised Domain Adaptation (CVPR 2024 · 44 citations) |
| add feature decomposition regularization Lreg | Office-31, Office-Home, and VisDA OPDA · H-score (%) | 82.0 -> 84.5; 73.6 -> 74.5; 61.5 -> 67.3 | Helped | LEAD: Learning Decomposition for Source-free Universal Domain Adaptation (CVPR 2024 · 34 citations) |
| transfer source-domain knowledge | Office-Home A→R · mAP | 4.51 -> 71.18 | Helped | Effective Comparative Prototype Hashing for Unsupervised Domain Adaptation (AAAI 2024 · 27 citations) |
| add discriminator Mixup to PL-Mix | Amazon Reviews 12-task average · accuracy | 93.41 -> 93.84 | Helped | On Unsupervised Domain Adaptation: Pseudo Label Guided Mixup for Adversarial Prompt Tuning (AAAI 2024 · 9 citations) |
| replace CLIP zero-shot hard-label guidance with source-enhanced pseudo-labels | Office-Home target Ar · accuracy | 47.6 -> 75.8 | Helped | Preserving Clusters in Prompt Learning for Unsupervised Domain Adaptation (CVPR 2025 · 7 citations) |
| Remove Lpse | Office-31 OSDA · HOS | 91.4 -> 87.3 | Hurt | Dynamic Target Distribution Estimation for Source-Free Open-Set Domain Adaptation (AAAI 2025 · 6 citations) |
| Add group reweighted pseudo-label refinement | open-set · accuracy | 50.4 -> 56.8 | Helped | Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias (CVPR 2024 · 6 citations) |
| add collaborative self-training to source-only training | Daily-DA, A→H · accuracy (%) | 49.6 -> 60.8 | Helped | Unsupervised Video Domain Adaptation with Masked Pre-Training and Collaborative Self-Training (CVPR 2024 · 6 citations) |
| Train prompts using target pseudo-label loss only instead of zero-shot CLIP | ImageCLEF · accuracy | 88.1 -> 90.1 | Helped | Enhancing Domain Adaptation through Prompt Gradient Alignment (NeurIPS 2024 · 6 citations) |
| Add prototypical contrastive learning (LPC) | Office-Home (Avg.) · accuracy | 30.45 -> 37.13 | Helped | Link-based Contrastive Learning for One-Shot Unsupervised Domain Adaptation (CVPR 2025 · 4 citations) |
| add TASC loss and UniMS to CE training | Office OPDA · H-score | 82.9 -> 91.3 | Helped | Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation (AAAI 2025 · 3 citations) |
Baseline numbers that disagree
The same baseline on the same benchmark and metric, reported more than 5% apart by different papers here. Training budgets, data splits and reimplementations differ, so state which number you compare with.
| Baseline | Benchmark | Metric | Reported | Papers |
|---|---|---|---|---|
| GLC | VisDA OSDA | H-score (%) | 72.5 – 81.6 | 2 papers, e.g. CVPR 2024 and ICLR 2024 |
| GLC | Office-Home OSDA | H-score (%) | 69.8 – 76.2 | 2 papers, e.g. CVPR 2024 and ICLR 2024 |
| Baseline-B | DomainNet | average accuracy | 36.2 – 38.5 | 2 papers, e.g. ICLR 2022 and CVPR 2022 |
What reviewers will expect
Datasets and baselines named in the 12 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 · 9 of 12 papers
- DomainNet · 8 of 12 papers
- Office-31 · 5 of 12 papers
- VisDA · 5 of 12 papers
- VisDA-2017 · 4 of 12 papers
- Office · 2 of 12 papers
- PACS · 2 of 12 papers
Baselines
- DANN · 5 of 12 papers
- CDAN · 4 of 12 papers
- UAN · 3 of 12 papers
- CMU · 3 of 12 papers
- DCC · 3 of 12 papers
- OVANet · 3 of 12 papers
- DANCE · 3 of 12 papers
- SHOT · 3 of 12 papers
Recent preprints
Loading recent arXiv preprints…
Who publishes on it
Institutions on the most papers of this approach since 2023 (of 47 papers; from OpenAlex and the papers' affiliations).
- National University of Singapore · 6 papers
- Chinese Academy of Sciences · 5 papers
- Tongji University · 4 papers
- Nanyang Technological University · 4 papers
- University of Electronic Science and Technology of China · 3 papers
- A*STAR · 3 papers
- Xi’an Jiaotong University · 2 papers
- Alibaba Group · 2 papers
- Zhejiang University · 2 papers
- University of Technology Sydney · 2 papers
Use it
Download BibTeX of 40 key papersOpen the topic in the interactive map
Get alerts. Describe your idea in scime atlas and save it (free scime account): you get a weekly email when new preprints land close to it.
Related approaches
Same keyword cluster
- Conditional distribution alignment (162 papers)
- Source-free domain adaptation (92 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).