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
MedAI at SemEval-2021 Task 10: Negation-aware Pre-training for Source-free Negation Detection Domain Adaptation—0
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation—0
MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach For Indoor Localization—0
Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation—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
Prior-guided Source-free Domain Adaptation for Human Pose Estimation—0
Probability Distribution Alignment and Low-Rank Weight Decomposition for Source-Free Domain Adaptive Brain Decoding—0
Revisiting Source-Free Domain Adaptation: Insights into Representativeness, Generalization, and Variety—0
AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation—0
Self-Adapter at SemEval-2021 Task 10: Entropy-based Pseudo-Labeler for Source-free Domain Adaptation—0
Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation—0
Semantic Image Segmentation: Two Decades of Research—0
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation—0
Semi-Supervised Transfer Boosting (SS-TrBoosting)—0
SepRep-Net: Multi-source Free Domain Adaptation via Model Separation And Reparameterization—0
Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping—0
Source-Free Domain Adaptation for Real-world Image Dehazing—0
Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers—0
Source-Free Domain Adaptation for Semantic Segmentation—0
Source -Free Domain Adaptation for Speaker Verification in Data-Scarce Languages and Noisy Channels—0
Source-free Domain Adaptation for Video Object Detection Under Adverse Image Conditions—0
Source-free Domain Adaptation Requires Penalized Diversity—0
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics—0
Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation—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