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 51–75 of 188 papers

TitleStatusHype
Anatomy-guided domain adaptation for 3D in-bed human pose estimationCode1
Exploiting the Intrinsic Neighborhood Structure for Source-free Domain AdaptationCode1
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain AdaptationCode1
Upcycling Models under Domain and Category ShiftCode1
Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast LearningCode1
Sign Segmentation with Changepoint-Modulated Pseudo-LabellingCode1
Confident Anchor-Induced Multi-Source Free Domain AdaptationCode1
ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain AdaptationCode1
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate SegmentationCode1
Tent: Fully Test-time Adaptation by Entropy MinimizationCode1
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic SegmentationCode1
GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain AdaptationCode1
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition—0
FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions—0
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation—0
Feed-Forward Latent Domain Adaptation—0
Consistency Regularization for Generalizable Source-free Domain Adaptation—0
Feed-Forward Source-Free Domain Adaptation via Class Prototypes—0
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data—0
Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo—0
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets—0
Exploring Domain-Invariant Parameters for Source Free Domain Adaptation—0
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation—0
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation—0
MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach For Indoor Localization—0
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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