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 5175 of 188 papers

TitleStatusHype
Source-Free Domain Adaptation for Image SegmentationCode1
Generalized Source-free Domain AdaptationCode1
Source-Free Adaptation to Measurement Shift via Bottom-Up Feature RestorationCode1
Source-free Domain Adaptation via Avatar Prototype Generation and AdaptationCode1
Transformer-Based Source-Free Domain AdaptationCode1
Sign Segmentation with Changepoint-Modulated Pseudo-LabellingCode1
Source Data-absent Unsupervised Domain Adaptation through Hypothesis Transfer and Labeling TransferCode1
SS-SFDA : Self-Supervised Source-Free Domain Adaptation for Road Segmentation in Hazardous EnvironmentsCode1
Casting a BAIT for Offline and Online Source-free Domain AdaptationCode1
Tent: Fully Test-time Adaptation by Entropy MinimizationCode1
Universal Source-Free Domain AdaptationCode1
Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain AdaptationCode1
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
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition0
Label Calibration in Source Free Domain Adaptation0
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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