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Unsupervised Domain Adaptation On Visda2017

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
PDA (CLIP, ViT-B/16)89.7Prompt-based Distribution Alignment for Unsupervised Domain Adaptation-
DeepJDOT66.9DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation-
Implicit Alignment (with MDD)75.8Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation-
PMTrans88.8Patch-Mix Transformer for Unsupervised Domain Adaptation: A Game Perspective-
DisClusterDA-Unsupervised Domain Adaptation via Distilled Discriminative Clustering-
TransAdapter91.2TransAdapter: Vision Transformer for Feature-Centric Unsupervised Domain Adaptation-
SSRT-B (ours)88.76Safe Self-Refinement for Transformer-based Domain Adaptation-
RCL93.2Empowering Source-Free Domain Adaptation via MLLM-Guided Reliability-Based Curriculum Learning-
SAMB90.41Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation-
SFDA288.1SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation-
PDA (CLIP, ResNet-101)86.4Prompt-based Distribution Alignment for Unsupervised Domain Adaptation-
SFDA2++89.6SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation-
FFTAT93.8Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation-
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Unsupervised Domain Adaptation On Visda2017 | SOTA | HyperAI