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 126–150 of 188 papers

TitleStatusHype
Exploring Domain-Invariant Parameters for Source Free Domain Adaptation—0
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation—0
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation—0
ElimPCL: Eliminating Noise Accumulation with Progressive Curriculum Labeling for Source-Free Domain Adaptation—0
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement—0
Plug-and-play Shape Refinement Framework for Multi-site and Lifespan Brain Skull Stripping—0
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision—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
Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation—0
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey—0
A Comprehensive Survey on Source-free Domain Adaptation—0
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation—0
Source-free Domain Adaptation Requires Penalized Diversity—0
YNU-HPCC at SemEval-2021 Task 10: Using a Transformer-based Source-Free Domain Adaptation Model for Semantic Processing—0
Source-free Domain Adaptation via Distributional Alignment by Matching Batch Normalization Statistics—0
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation—0
Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation—0
Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation—0
Source Free Domain Adaptation with Image Translation—0
Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation—0
Consistency Regularization for Generalizable 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