Papers Multi-Source Unsupervised Domain Adaptation
“Multi-Source Unsupervised Domain Adaptation” 태그가 달린 논문 46편 · 필터 해제
Robust Indoor Localization in Dynamic Environments: A Multi-source Unsupervised Domain Adaptation Framework
Fingerprint localization has gained significant attention due to its cost-effective deployment, low complexity, and high efficacy. However, traditional methods, while effective for static data, often struggle in dynamic …
Domain AdaptationIndoor LocalizationMulti-Source Unsupervised Domain AdaptationTransfer Learning+1Multi-Source Unsupervised Domain Adaptation with Prototype Aggregation
Multi-source domain adaptation (MSDA) plays an important role in industrial model generalization. Recent efforts on MSDA focus on enhancing multi-domain distributional alignment while omitting three issues, e.g., the cla…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationPseudo LabelUnsupervised Domain AdaptationGradual Fine-Tuning with Graph Routing for Multi-Source Unsupervised Domain Adaptation
Multi-source unsupervised domain adaptation aims to leverage labeled data from multiple source domains for training a machine learning model to generalize well on a target domain without labels. Source domain selection p…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationNatural Language InferenceSentiment Analysis+1BTMuda: A Bi-level Multi-source unsupervised domain adaptation framework for breast cancer diagnosis
Deep learning has revolutionized the early detection of breast cancer, resulting in a significant decrease in mortality rates. However, difficulties in obtaining annotations and huge variations in distribution between tr…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationMulti-source Unsupervised Domain Adaptation on Graphs with Transferability Modeling
In this paper, we tackle a new problem of \textit{multi-source unsupervised domain adaptation (MSUDA) for graphs}, where models trained on annotated source domains need to be transferred to the unsupervised target graph …
Domain AdaptationMeta-LearningMulti-Source Unsupervised Domain AdaptationNode Classification+1Enhancing Domain Adaptation through Prompt Gradient Alignment
Prior Unsupervised Domain Adaptation (UDA) methods often aim to train a domain-invariant feature extractor, which may hinder the model from learning sufficiently discriminative features. To tackle this, a line of works b…
Domain AdaptationLanguage ModelingLanguage ModellingMulti-Source Unsupervised Domain Adaptation+2AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation
Adversarial example detection, which can be conveniently applied in many scenarios, is important in the area of adversarial defense. Unfortunately, existing detection methods suffer from poor generalization performance, …
Adversarial AttackAdversarial DefenseDomain AdaptationMulti-Source Unsupervised Domain Adaptation+1A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition
Accurate recognition of human motion intention (HMI) is beneficial for exoskeleton robots to improve the wearing comfort level and achieve natural human-robot interaction. A classifier trained on labeled source subjects …
Domain AdaptationIntent DetectionMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationMulti-Source Domain Adaptation for Object Detection with Prototype-based Mean-teacher
Adapting visual object detectors to operational target domains is a challenging task, commonly achieved using unsupervised domain adaptation (UDA) methods. Recent studies have shown that when the labeled dataset comes fr…
Domain AdaptationMulti-Source Unsupervised Domain Adaptationobject-detectionObject Detection+1Distributionally Robust Learning for Multi-source Unsupervised Domain Adaptation
Empirical risk minimization often performs poorly when the distribution of the target domain differs from those of source domains. To address such potential distribution shifts, we develop an unsupervised domain adaptati…
Domain AdaptationFederated LearningMulti-Source Unsupervised Domain AdaptationPrediction+1Benchmarking Domain Adaptation for Chemical Processes on the Tennessee Eastman Process
In system monitoring, automatic fault diagnosis seeks to infer the systems' state based on sensor readings, e.g., through machine learning models. In this context, it is of key importance that, based on historical data, …
BenchmarkingDomain AdaptationFault DiagnosisMulti-Source Unsupervised Domain Adaptation+1MS3D++: Ensemble of Experts for Multi-Source Unsupervised Domain Adaption in 3D Object Detection
Deploying 3D detectors in unfamiliar domains has been demonstrated to result in a drastic drop of up to 70-90% in detection rate due to variations in lidar, geographical region, or weather conditions from their original …
3D Object DetectionDomain AdaptationDomain GeneralizationMulti-Source Unsupervised Domain Adaptation+3Multi-Source Domain Adaptation through Dataset Dictionary Learning in Wasserstein Space
This paper seeks to solve Multi-Source Domain Adaptation (MSDA), which aims to mitigate data distribution shifts when transferring knowledge from multiple labeled source domains to an unlabeled target domain. We propose …
Dictionary LearningDomain AdaptationMulti-Source Unsupervised Domain AdaptationDynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation
Multi-source unsupervised domain adaptation (MUDA) aims to transfer knowledge from related source domains to an unlabeled target domain. While recent MUDA methods have shown promising results, most focus on aligning the …
Domain AdaptationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationFACT: Federated Adversarial Cross Training
Federated Learning (FL) facilitates distributed model development to aggregate multiple confidential data sources. The information transfer among clients can be compromised by distributional differences, i.e., by non-i.i…
Domain AdaptationFederated LearningMulti-Source Unsupervised Domain AdaptationSource-Free Domain Adaptation+1Multi-Prompt Alignment for Multi-Source Unsupervised Domain Adaptation
Most existing methods for unsupervised domain adaptation (UDA) rely on a shared network to extract domain-invariant features. However, when facing multiple source domains, optimizing such a network involves updating the …
Domain AdaptationMulti-Source Unsupervised Domain AdaptationPrompt LearningUnsupervised Domain AdaptationJoint Attention-Driven Domain Fusion and Noise-Tolerant Learning for Multi-Source Domain Adaptation
As a study on the efficient usage of data, Multi-source Unsupervised Domain Adaptation transfers knowledge from multiple source domains with labeled data to an unlabeled target domain. However, the distribution discrepan…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationMulti-Source Unsupervised Domain Adaptation via Pseudo Target Domain
Multi-source domain adaptation (MDA) aims to transfer knowledge from multiple source domains to an unlabeled target domain. MDA is a challenging task due to the severe domain shift, which not only exists between target a…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationAligning Domain-specific Distribution and Classifier for Cross-domain Classification from Multiple Sources
While Unsupervised Domain Adaptation (UDA) algorithms, i.e., there are only labeled data from source domains, have been actively studied in recent years, most algorithms and theoretical results focus on Single-source Uns…
Domain Adaptationdomain classificationimage-classificationImage Classification+2Seeking Similarities over Differences: Similarity-based Domain Alignment for Adaptive Object Detection
In order to robustly deploy object detectors across a wide range of scenarios, they should be adaptable to shifts in the input distribution without the need to constantly annotate new data. This has motivated research in…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationObject DetectionUnsupervised Domain Adaptation