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 141150 of 188 papers

TitleStatusHype
Test-Time Adaptation for Visual Document Understanding0
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain AdaptationCode1
Active Source Free Domain Adaptation0
Attracting and Dispersing: A Simple Approach for Source-free Domain AdaptationCode1
A Comparison of Strategies for Source-Free Domain AdaptationCode0
Source-Free Domain Adaptation via Distribution EstimationCode0
Jacobian Norm for Unsupervised Source-Free Domain Adaptation0
BMD: A General Class-balanced Multicentric Dynamic Prototype Strategy for Source-free Domain AdaptationCode1
Instance Relation Graph Guided Source-Free Domain Adaptive Object DetectionCode1
Source-free Video Domain Adaptation by Learning Temporal Consistency for Action RecognitionCode1
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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