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 76–100 of 188 papers

TitleStatusHype
Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation—0
Discriminative Pattern Calibration Mechanism for Source-Free Domain Adaptation—0
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation—0
Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation—0
ElimPCL: Eliminating Noise Accumulation with Progressive Curriculum Labeling for Source-Free Domain Adaptation—0
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation—0
Exploiting Negative Learning for Implicit Pseudo Label Rectification in Source-Free Domain Adaptive Semantic Segmentation—0
Exploring Domain-Invariant Parameters for Source Free Domain Adaptation—0
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data—0
Feed-Forward Source-Free Domain Adaptation via Class Prototypes—0
Feed-Forward Latent Domain Adaptation—0
FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions—0
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition—0
Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation—0
Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation—0
IITK at SemEval-2021 Task 10: Source-Free Unsupervised Domain Adaptation using Class Prototypes—0
Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment—0
Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition—0
In Search for a Generalizable Method for Source Free Domain Adaptation—0
Jacobian Norm for Unsupervised Source-Free Domain Adaptation—0
Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification—0
Label Calibration in Source Free Domain Adaptation—0
Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation—0
Local-Global Pseudo-label Correction for Source-free Domain Adaptive Medical Image Segmentation—0
Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification—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