SOTAVerified

Source-Free Domain Adaptation

Source-Free Domain Adaptation (SFDA) is a domain adaptation method in machine learning and computer vision where the goal is to adapt a pre-trained model to a new, target domain without access to the source domain data. This approach is advantageous in scenarios where sharing the source data is impractical due to privacy concerns, data size, or proprietary restrictions

Papers

Showing 51–60 of 188 papers

TitleStatusHype
Agile Multi-Source-Free Domain AdaptationCode0
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets—0
Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo—0
SepRep-Net: Multi-source Free Domain Adaptation via Model Separation And Reparameterization—0
Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive DistillationCode0
Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation—0
CNG-SFDA:Clean-and-Noisy Region Guided Online-Offline Source-Free Domain AdaptationCode0
De-Confusing Pseudo-Labels in Source-Free Domain AdaptationCode0
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective—0
Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RCLAccuracy93.2—Unverified
2SFDA2++Accuracy89.6—Unverified
3SPMAccuracy89.4—Unverified
4SFDA2Accuracy88.1—Unverified
5C-SFDAAccuracy87.8—Unverified
6DaCAccuracy87.3—Unverified
7SHOT++Accuracy87.3—Unverified
8NRCAccuracy85.9—Unverified
9G-SFDAAccuracy85.4—Unverified
10SHOTAccuracy82.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SPMAverage Accuracy86.7—Unverified
2DRAAverage Accuracy84—Unverified
3NELAverage Accuracy72.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CMAmIoU69.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CMAmIoU53.6—Unverified