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 6170 of 188 papers

TitleStatusHype
Contrastive Model Adaptation for Cross-Condition Robustness in Semantic SegmentationCode1
GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain AdaptationCode1
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition0
FREST: Feature RESToration for Semantic Segmentation under Multiple Adverse Conditions0
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation0
Feed-Forward Latent Domain Adaptation0
Consistency Regularization for Generalizable Source-free Domain Adaptation0
Feed-Forward Source-Free Domain Adaptation via Class Prototypes0
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data0
Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification0
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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