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SOTA
域适应
Domain Adaptation On Visda2017
Domain Adaptation On Visda2017
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
FFTAT
93.8
Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation
RCL
93.2
Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning
MIC
92.8
MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
SWG
92.7
Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidance
CMKD
91.8
Unsupervised Domain Adaption Harnessing Vision-Language Pre-training
-
DePT
90.7
Visual Prompt Tuning for Test-time Domain Adaptation
-
SDAT(ViT)
89.8
A Closer Look at Smoothness in Domain Adversarial Training
SFDA2++
89.6
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
PMtrans
88.8
Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective
-
CoVi
88.5
Contrastive Vicinal Space for Unsupervised Domain Adaptation
CDTrans
88.4
CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation
SFDA2
88.1
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
CAN
87.2
Contrastive Adaptation Network for Unsupervised Domain Adaptation
FixBi
87.2
FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation
dSNE
86.15
d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding
-
CPGA
86.0
Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation
Mean teacher
85.4
Self-ensembling for visual domain adaptation
MCC+NWD
83.7
Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation
SHOT
82.9
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation
DTA
81.5
Drop to Adapt: Learning Discriminative Features for Unsupervised Domain Adaptation
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