Universal Domain Adaptation
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Benchmarks
Most implemented
Upcycling Models under Domain and Category Shift
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning
LEAD: Learning Decomposition for Source-free Universal Domain Adaptation
OVANet: One-vs-All Network for Universal Domain Adaptation
Papers
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 Adaptation