Papers Multi-Source Unsupervised Domain Adaptation
“Multi-Source Unsupervised Domain Adaptation” 태그가 달린 논문 46편 · 필터 해제
DaSeGAN: Domain Adaptation for Segmentation Tasks via Generative Adversarial Networks
A weakness of deep learning methods is that they can fail when there is a mismatch between source and target data domains. In medical image applications, this is a common situation when data from new vendor devices or di…
Domain AdaptationDomain GeneralizationImage SegmentationMulti-Source Unsupervised Domain Adaptation+2Improving Transferability of Domain Adaptation Networks Through Domain Alignment Layers
Deep learning (DL) has been the primary approach used in various computer vision tasks due to its relevant results achieved on many tasks. However, on real-world scenarios with partially or no labeled data, DL methods ar…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationMulti-Source domain adaptation via supervised contrastive learning and confident consistency regularization
Multi-Source Unsupervised Domain Adaptation (multi-source UDA) aims to learn a model from several labeled source domains while performing well on a different target domain where only unlabeled data are available at train…
Contrastive LearningDomain AdaptationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationSecure Domain Adaptation with Multiple Sources
Multi-source unsupervised domain adaptation (MUDA) is a framework to address the challenge of annotated data scarcity in a target domain via transferring knowledge from multiple annotated source domains. When the source …
Domain AdaptationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationWasserstein Barycenter for Multi-Source Domain Adaptation
Multi-source domain adaptation is a key technique that allows a model to be trained on data coming from various probability distribution. To overcome the challenges posed by this learning scenario, we propose a metho…
Domain AdaptationFace RecognitionMulti-Source Unsupervised Domain AdaptationMusic Genre Recognition+2Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification
Deep learning-based multi-source unsupervised domain adaptation (MUDA) has been actively studied in recent years. Compared with single-source unsupervised domain adaptation (SUDA), domain shift in MUDA exists not only be…
ClassificationDomain AdaptationMulti-Source Unsupervised Domain AdaptationPseudo Label+1MOST: Multi-Source Domain Adaptation via Optimal Transport for Student-Teacher Learning
Multi-source domain adaptation (DA) is more challenging than conventional DA because the knowledge is transferred from several source domains to a target domain. To this end, we propose in this paper a novel model for mu…
Domain AdaptationImitation LearningMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationYour Classifier can Secretly Suffice Multi-Source Domain Adaptation
Multi-Source Domain Adaptation (MSDA) deals with the transfer of task knowledge from multiple labeled source domains to an unlabeled target domain, under a domain-shift. Existing methods aim to minimize this domain-shift…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationMulti-Source Domain Adaptation with Collaborative Learning for Semantic Segmentation
Multi-source unsupervised domain adaptation~(MSDA) aims at adapting models trained on multiple labeled source domains to an unlabeled target domain. In this paper, we propose a novel multi-source domain adaptation framew…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationSemantic SegmentationUnsupervised Domain AdaptationCollaborative Optimization and Aggregation for Decentralized Domain Generalization and Adaptation
Contemporary domain generalization (DG) and multi-source unsupervised domain adaptation (UDA) methods mostly collect data from multiple domains together for joint optimization. However, this centralized training para…
Domain AdaptationDomain GeneralizationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationSTEM: An Approach to Multi-Source Domain Adaptation With Guarantees
Multi-source Domain Adaptation (MSDA) is more practical but challenging than the conventional unsupervised domain adaptation due to the involvement of diverse multiple data sources. Two fundamental challenges of MSDA…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationKD3A: Unsupervised Multi-Source Decentralized Domain Adaptation via Knowledge Distillation
Conventional unsupervised multi-source domain adaptation (UMDA) methods assume all source domains can be accessed directly. This neglects the privacy-preserving policy, that is, all the data and computations must be kept…
Domain AdaptationKnowledge DistillationMulti-Source Unsupervised Domain AdaptationPrivacy Preserving+1Learning to Combine: Knowledge Aggregation for Multi-Source Domain Adaptation
Transferring knowledges learned from multiple source domains to target domain is a more practical and challenging task than conventional single-source domain adaptation. Furthermore, the increase of modalities brings mor…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationCurriculum Manager for Source Selection in Multi-Source Domain Adaptation
The performance of Multi-Source Unsupervised Domain Adaptation depends significantly on the effectiveness of transfer from labeled source domain samples. In this paper, we proposed an adversarial agent that learns a dyna…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationUnsupervised Domain AdaptationAdversarial Training Based Multi-Source Unsupervised Domain Adaptation for Sentiment Analysis
Multi-source unsupervised domain adaptation (MS-UDA) for sentiment analysis (SA) aims to leverage useful information in multiple source domains to help do SA in an unlabeled target domain that has no supervised informati…
Domain AdaptationMulti-Source Unsupervised Domain AdaptationSentiment AnalysisTransfer Learning+1TriGAN: Image-to-Image Translation for Multi-Source Domain Adaptation
Most domain adaptation methods consider the problem of transferring knowledge to the target domain from a single source dataset. However, in practical applications, we typically have access to multiple sources. In this p…
Domain AdaptationImage-to-Image TranslationMulti-Source Unsupervised Domain AdaptationTranslationMulti-source Attention for Unsupervised Domain Adaptation
Domain adaptation considers the problem of generalising a model learnt using data from a particular source domain to a different target domain. Often it is difficult to find a suitable single source to adapt from, and on…
Domain AdaptationGeneral ClassificationMulti-Source Unsupervised Domain AdaptationSentiment Analysis+2Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation
Domain adaptation (DA) is the topical problem of adapting models from labelled source datasets so that they perform well on target datasets where only unlabelled or partially labelled data is available. Many methods have…
Domain AdaptationMeta-LearningMMEMulti-Source Unsupervised Domain Adaptation+2Domain Adaptive Ensemble Learning
The problem of generalizing deep neural networks from multiple source domains to a target one is studied under two settings: When unlabeled target data is available, it is a multi-source unsupervised domain adaptation (U…
Domain AdaptationDomain GeneralizationEnsemble LearningMulti-Source Unsupervised Domain Adaptation+2Multi-source Distilling Domain Adaptation
Deep neural networks suffer from performance decay when there is domain shift between the labeled source domain and unlabeled target domain, which motivates the research on domain adaptation (DA). Conventional DA methods…
Domain AdaptationMulti-Source Unsupervised Domain Adaptation