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SOTA
无源域适应
Source Free Domain Adaptation On Visda 2017
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
Columns
模型名称
Accuracy
Paper Title
Repository
RCL
93.2
Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning
SFDA2++
89.6
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
SFDA2
88.1
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
C-SFDA
87.8
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
SHOT++
87.3
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling Transfer
DaC
87.3
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning
NRC
85.9
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation
G-SFDA
85.4
Generalized Source-free Domain Adaptation
SHOT
82.9
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
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Source Free Domain Adaptation On Visda 2017 | SOTA | HyperAI超神经