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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.
Enhancing Source-Free Domain Adaptive Object Detection with Low-confidence Pseudo Label Distillation
Simplifying Source-Free Domain Adaptation for Object Detection: Effective Self-Training Strategies and Performance Insights
An Uncertainty-guided Tiered Self-training Framework for Active Source-free Domain Adaptation in Prostate Segmentation
Source -Free Domain Adaptation for Speaker Verification in Data-Scarce Languages and Noisy Channels
Evidentially Calibrated Source-Free Time-Series Domain Adaptation with Temporal Imputation
Proxy Denoising for Source-Free Domain Adaptation
Empowering Source-Free Domain Adaptation with MLLM-driven Curriculum Learning
SpGesture: Source-Free Domain-adaptive sEMG-based Gesture Recognition with Jaccard Attentive Spiking Neural Network
Source-Free Domain Adaptation Guided by Vision and Vision-Language Pre-Training
Source-Free Domain Adaptation of Weakly-Supervised Object Localization Models for Histology
Source-free Domain Adaptation for Video Object Detection Under Adverse Image Conditions
GLC++: Source-Free Universal Domain Adaptation through Global-Local Clustering and Contrastive Affinity Learning
When Cars meet Drones: Hyperbolic Federated Learning for Source-Free Domain Adaptation in Adverse Weather
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
Unified Source-Free Domain Adaptation
Agile Multi-Source-Free Domain Adaptation
Physics-informed and Unsupervised Riemannian Domain Adaptation for Machine Learning on Heterogeneous EEG Datasets
Selection, Ensemble, and Adaptation: Advancing Multi-Source-Free Domain Adaptation via Architecture Zoo
SepRep-Net: Multi-source Free Domain Adaptation via Model Separation And Reparameterization
Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation
Source-Free Domain Adaptation with Diffusion-Guided Source Data Generation
Source-free Domain Adaptive Object Detection in Remote Sensing Images
CNG-SFDA:Clean-and-Noisy Region Guided Online-Offline Source-Free Domain Adaptation
CLIP-Guided Source-Free Object Detection in Aerial Images
De-Confusing Pseudo-Labels in Source-Free Domain Adaptation
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation
Target-agnostic Source-free Domain Adaptation for Regression Tasks
Self-training solutions for the ICCV 2023 GeoNet Challenge
Aligning Non-Causal Factors for Transformer-Based Source-Free Domain Adaptation
Source-Free Domain Adaptation with Frozen Multimodal Foundation Model
Periodically Exchange Teacher-Student for Source-Free Object Detection
Annotator: A Generic Active Learning Baseline for LiDAR Semantic Segmentation
Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition
A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation
Robust Source-Free Domain Adaptation for Fundus Image Segmentation
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