Domain Adaptation On Visda2017

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Accuracy

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Leistungsergebnisse verschiedener Modelle zu diesem Benchmark

		Paper Title
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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Domain Adaptation On Visda2017

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