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

模型名称
Accuracy
Paper TitleRepository
CDAN73.7Conditional Adversarial Domain Adaptation-
CRST78.1Confidence Regularized Self-Training-
CAN87.2Contrastive Adaptation Network for Unsupervised Domain Adaptation-
MCC+NWD83.7Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation-
SWG92.7Combining inherent knowledge of vision-language models with unsupervised domain adaptation through strong-weak guidance-
PMtrans88.8Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective-
FixBi87.2FixBi: Bridging Domain Spaces for Unsupervised Domain Adaptation-
SENTRY (ResNet50)76.7SENTRY: Selective Entropy Optimization via Committee Consistency for Unsupervised Domain Adaptation-
JAN58.3Deep Transfer Learning with Joint Adaptation Networks-
SFDA2++89.6SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation-
CPGA86.0Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation-
Mean teacher85.4Self-ensembling for visual domain adaptation-
DeepJDOT66.9DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation-
CoVi88.5Contrastive Vicinal Space for Unsupervised Domain Adaptation-
DePT90.7Visual Prompt Tuning for Test-time Domain Adaptation-
CDTrans88.4CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation-
SFDA288.1SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation-
dSNE86.15d-SNE: Domain Adaptation Using Stochastic Neighborhood Embedding
MIC92.8MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation-
RCL93.2Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning-
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Domain Adaptation On Visda2017 | SOTA | HyperAI超神经