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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 126–150 of 188 papers

TitleStatusHype
Source Free Domain Adaptation with Image Translation—0
Source-Free Video Domain Adaptation With Spatial-Temporal-Historical Consistency Learning—0
Target-agnostic Source-free Domain Adaptation for Regression Tasks—0
TempT: Temporal consistency for Test-time adaptation—0
Test-Time Adaptation for Visual Document Understanding—0
Test-time Batch Statistics Calibration for Covariate Shift—0
The University of Arizona at SemEval-2021 Task 10: Applying Self-training, Active Learning and Data Augmentation to Source-free Domain Adaptation—0
Transcending Domains through Text-to-Image Diffusion: A Source-Free Approach to Domain Adaptation—0
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering—0
Uncertainty-Guided Mixup for Semi-Supervised Domain Adaptation without Source Data—0
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective—0
Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples—0
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision—0
Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey—0
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation—0
ViLAaD: Enhancing "Attracting and Dispersing'' Source-Free Domain Adaptation with Vision-and-Language Model—0
Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation—0
When Source-Free Domain Adaptation Meets Learning with Noisy Labels—0
YNU-HPCC at SemEval-2021 Task 10: Using a Transformer-based Source-Free Domain Adaptation Model for Semantic Processing—0
FACT: Federated Adversarial Cross TrainingCode0
Unveiling the Unknown: Unleashing the Power of Unknown to Known in Open-Set Source-Free Domain AdaptationCode0
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum LearningCode0
Rethinking the Role of Pre-Trained Networks in Source-Free Domain AdaptationCode0
EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse PrinciplesCode0
1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection TrackCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1RCLAccuracy93.2—Unverified
2SFDA2++Accuracy89.6—Unverified
3SPMAccuracy89.4—Unverified
4SFDA2Accuracy88.1—Unverified
5C-SFDAAccuracy87.8—Unverified
6DaCAccuracy87.3—Unverified
7SHOT++Accuracy87.3—Unverified
8NRCAccuracy85.9—Unverified
9G-SFDAAccuracy85.4—Unverified
10SHOTAccuracy82.9—Unverified
#ModelMetricClaimedVerifiedStatus
1SPMAverage Accuracy86.7—Unverified
2DRAAverage Accuracy84—Unverified
3NELAverage Accuracy72.4—Unverified
#ModelMetricClaimedVerifiedStatus
1CMAmIoU69.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CMAmIoU53.6—Unverified