Papers Source-Free Domain Adaptation
“Source-Free Domain Adaptation” 태그가 달린 논문 224편 · 필터 해제
Dual Co-Train: Cross-Dataset Ultrasound Tongue Segmentation Under Extreme Data Scarcity
Ultrasound tongue contour segmentation remains challenging under cross-dataset domain shift, where limited annotations, probe variability, and acquisition noise often degrade model generalization. We present a source-fre…
Source-Free Domain AdaptationTestMate: Test-Time Domain Adaptation Aided by Lightweight Vision Foundation Model
Test-Time Domain Adaptation (TTDA) aims to adapt Deep Neural Networks to distribution shifts using only streaming, unlabeled test data in real time. Current methods for semantic segmentation tasks suffer from critical li…
Source-Free Domain AdaptationSemantic SegmentationInstance SegmentationBrainRiem: Riemannian Prototype Learning for Source-Free Cross-Site Brain Network Diagnosis
Multi-site functional MRI (fMRI) studies are essential for robust neuropsychiatric diagnosis yet suffer severe domain shifts from scanner heterogeneity, demographics, and site-specific acquisition protocols. Traditional …
Source-Free Domain AdaptationGRAPH DOMAIN ADAPTATIONTemporal-Spectral Alignment with Frequency Adaptation for Source-Free Time-Series Adaptation
The goal of source-free domain adaptation (SFDA) for time-series data is to transfer knowledge from a pre-trained source model to an unlabeled target domain without requiring access to source data, while addressing featu…
Source-Free Domain AdaptationRethinking the Need for Source Models: Source-Free Domain Adaptation from Scratch Guided by a Vision-Language Model
Source-Free Domain Adaptation (SFDA) adapts source models to target domains without accessing source data, addressing privacy and transmission issues. However, existing methods still initialize from a source pre-trained …
Source-Free Domain AdaptationFoundation Model Guided Dual-Branch Co-Adaptation for Source-Free EEG Decoding
Source-free domain adaptation (SFDA) provides a practical solution to cross-subject EEG decoding by adapting source-pretrained models to unlabeled target domains without accessing source data. However, existing SFDA meth…
Source-Free Domain AdaptationKnowledge DistillationDomain GeneralizationEmotion RecognitionSource-Free Domain Adaptation with Vision-Language Prior
Source-Free Domain Adaptation (SFDA) seeks to adapt a source model, which is pre-trained on a supervised source domain, for a target domain, with only access to unlabeled target training data. Relying on pseudo labeling …
Source-Free Domain Adaptation$\oslash$ Source Models Leak What They Shouldn't $\nrightarrow$: Unlearning Zero-Shot Transfer in Domain Adaptation Through Adversarial Optimization
The increasing adaptation of vision models across domains, such as satellite imagery and medical scans, has raised an emerging privacy risk: models may inadvertently retain and leak sensitive source-domain specific infor…
Source-Free Domain AdaptationProCal: Probability Calibration for Neighborhood-Guided Source-Free Domain Adaptation
Source-Free Domain Adaptation (SFDA) adapts pre-trained models to unlabeled target domains without requiring access to source data. Although state-of-the-art methods leveraging local neighborhood structures show promise …
Source-Free Domain AdaptationForgetting-Resistant and Lesion-Aware Source-Free Domain Adaptive Fundus Image Analysis with Vision-Language Model
Source-free domain adaptation (SFDA) aims to adapt a model trained in the source domain to perform well in the target domain, with only unlabeled target domain data and the source model. Taking into account that conventi…
Source-Free Domain AdaptationClosing the Confusion Loop: CLIP-Guided Alignment for Source-Free Domain Adaptation
Source-Free Domain Adaptation (SFDA) tackles the problem of adapting a pre-trained source model to an unlabeled target domain without accessing any source data, which is quite suitable for the field of data security. Alt…
Source-Free Domain AdaptationContrastive LearningA Source-Free Approach for Domain Adaptation via Multiview Image Transformation and Latent Space Consistency
Domain adaptation (DA) addresses the challenge of transferring knowledge from a source domain to a target domain where image data distributions may differ. Existing DA methods often require access to source domain data, …
Source-Free Domain AdaptationSource-Free Domain Adaptation by Optimizing Batch-Wise Cosine Similarity
Source-Free Domain Adaptation (SFDA) is an emerging area of research that aims to adapt a model trained on a labeled source domain to an unlabeled target domain without accessing the source data. Most of the successful m…
Source-Free Domain AdaptationSource-Free Domain Adaptation for Geospatial Point Cloud Semantic Segmentation
Semantic segmentation of 3D geospatial point clouds is fundamental to remote sensing applications, yet domain shifts caused by regional and acquisition-related variations often degrade model performance. Although domain …
Unsupervised Domain AdaptationSource-Free Domain AdaptationSemantic SegmentationPoint CloudsSfMamba: Efficient Source-Free Domain Adaptation via Selective Scan Modeling
Source-free domain adaptation (SFDA) tackles the critical challenge of adapting source-pretrained models to unlabeled target domains without access to source data, overcoming data privacy and storage limitations in real-…
Source-Free Domain AdaptationComputational EfficiencyFedSCAl: Leveraging Server and Client Alignment for Unsupervised Federated Source-Free Domain Adaptation
We address the Federated source-Free Domain Adaptation (FFreeDA) problem, with clients holding unlabeled data with significant inter-client domain gaps. The FFreeDA setup constrains the FL frameworks to employ only a pre…
Source-Free Domain AdaptationCollaborative Learning with Multiple Foundation Models for Source-Free Domain Adaptation
Source-Free Domain Adaptation (SFDA) aims to adapt a pre-trained source model to an unlabeled target domain without access to source data. Recent advances in Foundation Models (FMs) have introduced new opportunities for …
Source-Free Domain AdaptationAligning What You Separate: Denoised Patch Mixing for Source-Free Domain Adaptation in Medical Image Segmentation
Source-Free Domain Adaptation (SFDA) is emerging as a compelling solution for medical image segmentation under privacy constraints, yet current approaches often ignore sample difficulty and struggle with noisy supervisio…
Source-Free Domain AdaptationMedical Image SegmentationDiffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation
Source-free domain adaptation (SFDA) is a challenging task that tackles domain shifts using only a pre-trained source model and unlabeled target data. Existing SFDA methods are restricted by the fundamental limitation of…
Source-Free Domain AdaptationAttention Residual Fusion Network with Contrast for Source-free Domain Adaptation
Source-free domain adaptation (SFDA) involves training a model on source domain and then applying it to a related target domain without access to the source data and labels during adaptation. The complexity of scene info…
Source-Free Domain Adaptation