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

Showing 125 of 188 papers

TitleStatusHype
SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image SegmentationCode0
Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain AdaptationCode0
DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image SegmentationCode0
Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-PromptingCode0
Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation0
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled DataCode0
Probability Distribution Alignment and Low-Rank Weight Decomposition for Source-Free Domain Adaptive Brain Decoding0
ElimPCL: Eliminating Noise Accumulation with Progressive Curriculum Labeling for Source-Free Domain Adaptation0
ViLAaD: Enhancing "Attracting and Dispersing'' Source-Free Domain Adaptation with Vision-and-Language Model0
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target DataCode0
MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach For Indoor Localization0
Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosisCode1
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition0
Label Calibration in Source Free Domain Adaptation0
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image SegmentationCode1
Revisiting Source-Free Domain Adaptation: Insights into Representativeness, Generalization, and Variety0
Prototypical Distillation and Debiased Tuning for Black-box Unsupervised Domain AdaptationCode0
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood ContextCode0
Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion AdaptationCode0
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data0
Semi-Supervised Transfer Boosting (SS-TrBoosting)0
Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification0
Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation0
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation0
Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation FrameworkCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RCLAccuracy93.2Unverified
2SFDA2++Accuracy89.6Unverified
3SPMAccuracy89.4Unverified
4SFDA2Accuracy88.1Unverified
5C-SFDAAccuracy87.8Unverified
6DaCAccuracy87.3Unverified
7SHOT++Accuracy87.3Unverified
8NRCAccuracy85.9Unverified
9G-SFDAAccuracy85.4Unverified
10SHOTAccuracy82.9Unverified
#ModelMetricClaimedVerifiedStatus
1SPMAverage Accuracy86.7Unverified
2DRAAverage Accuracy84Unverified
3NELAverage Accuracy72.4Unverified
#ModelMetricClaimedVerifiedStatus
1CMAmIoU69.1Unverified
#ModelMetricClaimedVerifiedStatus
1CMAmIoU53.6Unverified