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 8190 of 188 papers

TitleStatusHype
SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain AdaptationCode0
Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion AdaptationCode0
Agile Multi-Source-Free Domain AdaptationCode0
Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-PromptingCode0
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene SegmentationCode0
Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain AdaptationCode0
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target DataCode0
Self-training solutions for the ICCV 2023 GeoNet ChallengeCode0
Reconciling a Centroid-Hypothesis Conflict in Source-Free Domain AdaptationCode0
SemEval-2021 Task 10: Source-Free Domain Adaptation for Semantic ProcessingCode0
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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