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 111120 of 188 papers

TitleStatusHype
Self-training via Metric Learning for Source-Free Domain Adaptation of Semantic Segmentation0
Semantic Image Segmentation: Two Decades of Research0
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective0
Semi-Supervised Hypothesis Transfer for Source-Free Domain Adaptation0
Semi-Supervised Transfer Boosting (SS-TrBoosting)0
SepRep-Net: Multi-source Free Domain Adaptation via Model Separation And Reparameterization0
Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation0
Adaptive Adversarial Network for Source-Free Domain Adaptation0
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition0
Active 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