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Source Free Domain Adaptation On Visda 2017

评估指标

Accuracy

评测结果

各个模型在此基准测试上的表现结果

模型名称	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 via MLLM-Guided Reliability-Based 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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Source Free Domain Adaptation On Visda 2017 | SOTA | HyperAI超神经