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Unsupervised Domain Adaptation On Visda2017

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle
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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