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Universal Domain Adaptation On Visda2017

Metrics

H-score

Source-free

Results

Performance results of various models on this benchmark

Model Name	H-score	Source-free	Paper Title	Repository
OVANet	53.1	no	OVANet: One-vs-All Network for Universal Domain Adaptation	-
SAN	60.1	no	Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All in One Classifier
SHOT-O	44.0	yes	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation	-
DCC	43.0	no	Domain Consensus Clustering for Universal Domain Adaptation
LEAD	76.6	yes	LEAD: Learning Decomposition for Source-free Universal Domain Adaptation	-
MLNet	69.9	no	MLNet: Mutual Learning Network with Neighborhood Invariance for Universal Domain Adaptation	-
UniAM	65.18	no	Universal Domain Adaptation via Compressive Attention Matching	-
UMAD	58.3	yes	UMAD: Universal Model Adaptation under Domain and Category Shift	-
GATE	56.4	no	Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation	-
UniOT	57.32	no	Unified Optimal Transport Framework for Universal Domain Adaptation	-
GLC	73.1	yes	Upcycling Models under Domain and Category Shift	-
UAN	34.8	no	Universal Domain Adaptation	-
TASC	90.36	no	Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation

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Universal Domain Adaptation On Visda2017 | SOTA | HyperAI