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Source-Free Domain Adaptation
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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
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
SRPL-SFDA: SAM-Guided Reliable Pseudo-Labels for Source-Free Domain Adaptation in Medical Image Segmentation
Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation
DDFP: Data-dependent Frequency Prompt for Source Free Domain Adaptation of Medical Image Segmentation
Leveraging Segment Anything Model for Source-Free Domain Adaptation via Dual Feature Guided Auto-Prompting
Learning Compositional Transferability of Time Series for Source-Free Domain Adaptation
Context Aware Grounded Teacher for Source Free Object Detection
Effective Dual-Region Augmentation for Reduced Reliance on Large Amounts of Labeled Data
Probability Distribution Alignment and Low-Rank Weight Decomposition for Source-Free Domain Adaptive Brain Decoding
ElimPCL: Eliminating Noise Accumulation with Progressive Curriculum Labeling for Source-Free Domain Adaptation
ViLAaD: Enhancing "Attracting and Dispersing'' Source-Free Domain Adaptation with Vision-and-Language Model
Disentangled Source-Free Personalization for Facial Expression Recognition with Neutral Target Data
MTLoc: A Confidence-Based Source-Free Domain Adaptation Approach For Indoor Localization
Source-free domain adaptation based on label reliability for cross-domain bearing fault diagnosis
Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion Recognition
Label Calibration in Source Free Domain Adaptation
AIF-SFDA: Autonomous Information Filter-driven Source-Free Domain Adaptation for Medical Image Segmentation
Prototypical Distillation and Debiased Tuning for Black-box Unsupervised Domain Adaptation
Bridge then Begin Anew: Generating Target-relevant Intermediate Model for Source-free Visual Emotion Adaptation
Federated Source-free Domain Adaptation for Classification: Weighted Cluster Aggregation for Unlabeled Data
What Has Been Overlooked in Contrastive Source-Free Domain Adaptation: Leveraging Source-Informed Latent Augmentation within Neighborhood Context
Semi-Supervised Transfer Boosting (SS-TrBoosting)
Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification
Multi-Granularity Class Prototype Topology Distillation for Class-Incremental Source-Free Unsupervised Domain Adaptation
Unveiling the Superior Paradigm: A Comparative Study of Source-Free Domain Adaptation and Unsupervised Domain Adaptation
Recall and Refine: A Simple but Effective Source-free Open-set Domain Adaptation Framework
GALA: Graph Diffusion-based Alignment with Jigsaw for Source-free Domain Adaptation
Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation
Efficient Source-Free Time-Series Adaptation via Parameter Subspace Disentanglement
A3: Active Adversarial Alignment for Source-Free Domain Adaptation
Source-Free Domain Adaptation for YOLO Object Detection
SFDA-rPPG: Source-Free Domain Adaptive Remote Physiological Measurement with Spatio-Temporal Consistency
Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation
Low Saturation Confidence Distribution-based Test-Time Adaptation for Cross-Domain Remote Sensing Image Classification
EIANet: A Novel Domain Adaptation Approach to Maximize Class Distinction with Neural Collapse Principles
Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection
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