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
Consistency Regularization for Generalizable Source-free Domain Adaptation—0
Feed-Forward Source-Free Domain Adaptation via Class Prototypes—0
Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples—0
Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation—0
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene SegmentationCode0
FACT: Federated Adversarial Cross TrainingCode0
Source-Free Domain Adaptation for SSVEP-based Brain-Computer InterfacesCode0
Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers—0
Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment—0
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation—0
Source-free Domain Adaptation Requires Penalized Diversity—0
Few-shot Fine-tuning is All You Need for Source-free Domain AdaptationCode0
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic SegmentationCode0
TempT: Temporal consistency for Test-time adaptation—0
A Comprehensive Survey on Source-free Domain Adaptation—0
In Search for a Generalizable Method for Source Free Domain Adaptation—0
Semantic Image Segmentation: Two Decades of Research—0
When Source-Free Domain Adaptation Meets Learning with Noisy Labels—0
Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain AdaptationCode0
Chaos to Order: A Label Propagation Perspective on Source-Free Domain Adaptation—0
1st Place Solution for ECCV 2022 OOD-CV Challenge Object Detection TrackCode0
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation—0
Source-Free Video Domain Adaptation With Spatial-Temporal-Historical Consistency Learning—0
SSDA: Secure Source-Free Domain AdaptationCode0
Source-Free Domain Adaptation for Question Answering with Masked Self-trainingCode0
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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