Papers Universal Domain Adaptation
“Universal Domain Adaptation” 태그가 달린 논문 59편 · 필터 해제
GMM-COMET: Continual Source-Free Universal Domain Adaptation via a Mean Teacher and Gaussian Mixture Model-Based Pseudo-Labeling
Unsupervised domain adaptation tackles the problem that domain shifts between training and test data impair the performance of neural networks in many real-world applications. Thereby, in realistic scenarios, the source …
Unsupervised Domain AdaptationUniversal Domain AdaptationTraining-Free Label Space Alignment for Universal Domain Adaptation
Universal domain adaptation (UniDA) transfers knowledge from a labeled source domain to an unlabeled target domain, where label spaces may differ and the target domain may contain private classes. Previous UniDA methods …
Universal Domain AdaptationE-MLNet: Enhanced Mutual Learning for Universal Domain Adaptation with Sample-Specific Weighting
Universal Domain Adaptation (UniDA) seeks to transfer knowledge from a labeled source to an unlabeled target domain without assuming any relationship between their label sets, requiring models to classify known samples w…
Universal Domain AdaptationFeature-Space Planes Searcher: A Universal Domain Adaptation Framework for Interpretability and Computational Efficiency
Domain shift, characterized by degraded model performance during transition from labeled source domains to unlabeled target domains, poses a persistent challenge for deploying deep learning systems. Current unsupervised …
Unsupervised Domain AdaptationProtein Structure PredictionUniversal Domain AdaptationComputational EfficiencyTarget Semantics Clustering via Text Representations for Robust Universal Domain Adaptation
Universal Domain Adaptation (UniDA) focuses on transferring source domain knowledge to the target domain under both domain shift and unknown category shift. Its main challenge lies in identifying common class samples and…
Domain AdaptationUniversal Domain AdaptationAnalysis of Pseudo-Labeling for Online Source-Free Universal Domain Adaptation
A domain (distribution) shift between training and test data often hinders the real-world performance of deep neural networks, necessitating unsupervised domain adaptation (UDA) to bridge this gap. Online source-free UDA…
Domain AdaptationPseudo LabelUniversal Domain AdaptationUnsupervised Domain AdaptationOptimal Transport and Adaptive Thresholding for Universal Domain Adaptation on Time Series
Universal Domain Adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain, even when their classes are not fully shared. Few dedicated UniDA methods exist for Time Series (…
Domain AdaptationTime SeriesUniversal Domain AdaptationUniversal Domain Adaptation for Semantic Segmentation
Unsupervised domain adaptation for semantic segmentation (UDA-SS) aims to transfer knowledge from labeled synthetic data (source) to unlabeled real-world data (target). Traditional UDA-SS methods work on the assumpti…
Domain AdaptationPseudo LabelSemantic SegmentationUniversal Domain Adaptation+1Reducing Source-Private Bias in Extreme Universal Domain Adaptation
Universal Domain Adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain without assuming how much the label-sets of the two domains intersect. The goal of UniDA is to ach…
Domain AdaptationSelf-Supervised LearningUniversal Domain AdaptationPrototypical Partial Optimal Transport for Universal Domain Adaptation
Universal domain adaptation (UniDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain without requiring the same label sets of both domains. The existence of domain and category shift …
Domain AdaptationUniversal Domain AdaptationMemory-Efficient Pseudo-Labeling for Online Source-Free Universal Domain Adaptation using a Gaussian Mixture Model
In practice, domain shifts are likely to occur between training and test data, necessitating domain adaptation (DA) to adjust the pre-trained source model to the target domain. Recently, universal domain adaptation (UniD…
Domain AdaptationOut-of-Distribution DetectionUniversal Domain AdaptationGLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning
Deep neural networks often exhibit sub-optimal performance under covariate and category shifts. Source-Free Domain Adaptation (SFDA) presents a promising solution to this dilemma, yet most SFDA approaches are restricted …
ClusteringContrastive LearningDomain AdaptationSource-Free Domain Adaptation+1Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias
Domain adaptation is a critical task in machine learning that aims to improve model performance on a target domain by leveraging knowledge from a related source domain. In this work, we introduce Universal Semi-Supervise…
Domain AdaptationPseudo LabelSemi-supervised Domain AdaptationUniversal Domain AdaptationLEAD: Learning Decomposition for Source-free Universal Domain Adaptation
Universal Domain Adaptation (UniDA) targets knowledge transfer in the presence of both covariate and label shifts. Recently, Source-free Universal Domain Adaptation (SF-UniDA) has emerged to achieve UniDA without access …
Domain AdaptationTransfer LearningUniversal Domain AdaptationCOMET: Contrastive Mean Teacher for Online Source-Free Universal Domain Adaptation
In real-world applications, there is often a domain shift from training to test data. This observation resulted in the development of test-time adaptation (TTA). It aims to adapt a pre-trained source model to the test da…
Domain AdaptationTest-time AdaptationUniversal Domain AdaptationMLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation
Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowledge transfer. Existing UniDA methods ma…
Domain AdaptationTransfer LearningUniversal Domain AdaptationA Unified Framework for Unsupervised Domain Adaptation based on Instance Weighting
Despite the progress made in domain adaptation, solving Unsupervised Domain Adaptation (UDA) problems with a general method under complex conditions caused by label shifts between domains remains a formidable task. In th…
Domain AdaptationPartial Domain AdaptationUniversal Domain AdaptationUnsupervised Domain AdaptationUniversal Domain Adaptation for Robust Handling of Distributional Shifts in NLP
When deploying machine learning systems to the wild, it is highly desirable for them to effectively leverage prior knowledge to the unfamiliar domain while also firing alarms to anomalous inputs. In order to address thes…
Domain AdaptationUniversal Domain AdaptationMemory-Assisted Sub-Prototype Mining for Universal Domain Adaptation
Universal domain adaptation aims to align the classes and reduce the feature gap between the same category of the source and target domains. The target private category is set as the unknown class during the adaptation p…
Domain AdaptationUniversal Domain AdaptationCOCA: Classifier-Oriented Calibration via Textual Prototype for Source-Free Universal Domain Adaptation
Universal domain adaptation (UniDA) aims to address domain and category shifts across data sources. Recently, due to more stringent data restrictions, researchers have introduced source-free UniDA (SF-UniDA). SF-UniDA me…
Domain AdaptationFew-Shot LearningLanguage ModellingUniversal Domain Adaptation