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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 101–125 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
Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation—0
Unveiling the Unknown: Unleashing the Power of Unknown to Known in Open-Set Source-Free Domain AdaptationCode0
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective—0
Target-agnostic Source-free Domain Adaptation for Regression Tasks—0
Self-training solutions for the ICCV 2023 GeoNet ChallengeCode0
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation—0
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation—0
Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition—0
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation—0
SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain AdaptationCode0
Transcending Domains through Text-to-Image Diffusion: A Source-Free Approach to Domain Adaptation—0
Better Practices for Domain Adaptation—0
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering—0
Local-Global Pseudo-label Correction for Source-free Domain Adaptive Medical Image Segmentation—0
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation—0
Prior-guided Source-free Domain Adaptation for Human Pose Estimation—0
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision—0
Show:102550
← PrevPage 5 of 8Next →
All datasetsVisDA-2017PACSCityscapes to ACDCCityscapes to Dark Zurich

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