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Source Free Domain Adaptation On Visda 2017

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
SFDA2++	89.6	SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
RCL	93.2	Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning
NRC	85.9	Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
G-SFDA	85.4	Generalized Source-free Domain Adaptation
SHOT++	87.3	Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
SHOT	82.9	Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
SFDA2	88.1	SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
DaC	87.3	Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
C-SFDA	87.8	C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation

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