ArticleACM transactions on intelligent systems and technology2020
A Survey of Unsupervised Deep Domain Adaptation.
Article in ACM transactions on intelligent systems and technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 72 papers, 1 of them a synthesis that pooled it.
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Who cites it
72 citing papers in PubMed, 1 synthesis or guideline pooled it.
- The Micro-Mobility Sensing Gap: A Systematic Review of Physiological Safety Monitoring from Cycling to E-Scooters.Sensors (Basel, Switzerland) · 2026Pooled it
- Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine.Scientific reports · 2022Trial
- Keeping Medical AI Healthy and Trustworthy: A Review of Detection and Correction Methods for System Degradation.IEEE transactions on bio-medical engineering · 2026Review
- Dual-Model Artificial Intelligence Framework Integrating AI-Based Markerless Motion Capture for Dynamic Gait Prediction and Health-Status Classification.Diagnostics (Basel, Switzerland) · 2026Article
- Towards generalizable contactless oximetry from multispectral video via deep domain-adaptive learning strategy.Biomedical optics express · 2026Article
- FD-MSP: feature decoupling network with multi-scale prototypes for domain-adaptive polyp segmentation.BMC medical imaging · 2026Article
- Multi-Physics Monotone Score Transport for Unsupervised Domain Adaptation of Continuous Tool Wear Prediction.Sensors (Basel, Switzerland) · 2026Article
- N2G calibrator: a cross-subject adversarial learning framework for neural signal-driven gait tracking in Parkinson's disease.Communications engineering · 2026Article
- Uncertainty aware machine learning for bridging simulation and experiment in high throughput materials characterization.Scientific reports · 2026Article
- Kernel mean matching enhances risk estimation under spatial distribution shifts.Scientific reports · 2026Article
- Compact target-aware stability-based feature selection for cross-dataset wearable activity monitoring.PloS one · 2026Article
- A survey of machine learning for estimating workload: considering unknown tasks.Frontiers in robotics and AI · 2026Review
- Understanding generative AI output with embedding models.Science advances · 2025Article
- Phylogenetic Methods Meet Deep Learning.Genome biology and evolution · 2025Article
- A deep learning derived prostate zonal volume-based biomarker from T2-weighted MRI to distinguish between prostate cancer and benign prostatic hyperplasia.Medical physics · 2025Article
- Scalable evaluation framework for retrieval augmented generation in tobacco research using large Language models.Scientific reports · 2025Article
- scDCT: a conditional diffusion-based deep learning model for high-fidelity single-cell cross-modality translation.Briefings in bioinformatics · 2025Article
- A simple preprocessing approach for improving semantic segmentation in unsupervised domain adaptation.Scientific reports · 2025Article
- Canine EEG helps human: cross-species and cross-modality epileptic seizure detection via multi-space alignment.National science review · 2025Article
- Tool Wear State Identification Method with Variable Cutting Parameters Based on Multi-Source Unsupervised Domain Adaptation.Sensors (Basel, Switzerland) · 2025Article
12 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
Deep learning has produced state-of-the-art results for a variety of tasks. While such approaches for supervised learning have performed well, they assume that training and testing data are drawn from the same distribution, which may not always be the case. As a complement to this challenge, single-source unsupervised domain adaptation can handle situations where a network is trained on labeled data from a source domain and unlabeled data from a related but different target domain with the goal of performing well at test-time on the target domain. Many single-source and typically homogeneous unsupervised deep domain adaptation approaches have thus been developed, combining the powerful, hierarchical representations from deep learning with domain adaptation to reduce reliance on potentially-costly target data labels. This survey will compare these approaches by examining alternative methods, the unique and common elements, results, and theoretical insights. We follow this with a look at application areas and open research directions.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.