Home Console Docs News Papers Tutorials Datasets Wiki SOTA LLM Models GPU Leaderboard Events

English

Universal Domain Adaptation On Visda2017

Metrics

H-score

Source-free

Results

Performance results of various models on this benchmark

			Paper Title	Repository
TASC	90.36	no	Target Semantics Clustering via Text Representations for Robust Universal Domain Adaptation	-
LEAD	76.6	yes	LEAD: Learning Decomposition for Source-free Universal Domain Adaptation
GLC	73.1	yes	Upcycling Models under Domain and Category Shift
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	-
SAN	60.1	no	Boosting Novel Category Discovery Over Domains with Soft Contrastive Learning and All in One Classifier	-
UMAD	58.3	yes	UMAD: Universal Model Adaptation under Domain and Category Shift	-
UniOT	57.32	no	Unified Optimal Transport Framework for Universal Domain Adaptation
GATE	56.4	no	Geometric Anchor Correspondence Mining With Uncertainty Modeling for Universal Domain Adaptation	-
OVANet	53.1	no	OVANet: One-vs-All Network for Universal Domain Adaptation
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	-
UAN	34.8	no	Universal Domain Adaptation	-

0 of 13 row(s) selected.

Universal Domain Adaptation On Visda2017 | SOTA | HyperAI