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 1120 of 188 papers

TitleStatusHype
Confident Anchor-Induced Multi-Source Free Domain AdaptationCode1
Anatomy-guided domain adaptation for 3D in-bed human pose estimationCode1
Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive LearningCode1
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate SegmentationCode1
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration ErrorCode1
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain AdaptationCode1
Concurrent Subsidiary Supervision for Unsupervised Source-Free Domain AdaptationCode1
Cluster-level pseudo-labelling for source-free cross-domain facial expression recognitionCode1
BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain AdaptationCode1
Balancing Discriminability and Transferability for Source-Free Domain AdaptationCode1
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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