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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.
SIDE: Self-supervised Intermediate Domain Exploration for Source-free Domain Adaptation
Transcending Domains through Text-to-Image Diffusion: A Source-Free Approach to Domain Adaptation
UPL-SFDA: Uncertainty-aware Pseudo Label Guided Source-Free Domain Adaptation for Medical Image Segmentation
Better Practices for Domain Adaptation
Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering
Local-Global Pseudo-label Correction for Source-free Domain Adaptive Medical Image Segmentation
Domain-Specificity Inducing Transformers for Source-Free Domain Adaptation
Prior-guided Source-free Domain Adaptation for Human Pose Estimation
Unsupervised Adaptation of Polyp Segmentation Models via Coarse-to-Fine Self-Supervision
Cal-SFDA: Source-Free Domain-adaptive Semantic Segmentation with Differentiable Expected Calibration Error
ReCLIP: Refine Contrastive Language Image Pre-Training with Source Free Domain Adaptation
Consistency Regularization for Generalizable Source-free Domain Adaptation
Divide and Adapt: Active Domain Adaptation via Customized Learning
Feed-Forward Source-Free Domain Adaptation via Class Prototypes
Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning
Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples
Source-Free Domain Adaptation with Temporal Imputation for Time Series Data
Source-Free Domain Adaptive Fundus Image Segmentation with Class-Balanced Mean Teacher
Generating Reliable Pixel-Level Labels for Source Free Domain Adaptation
Learning Content-enhanced Mask Transformer for Domain Generalized Urban-Scene Segmentation
Hyperbolic Active Learning for Semantic Segmentation under Domain Shift
Towards Source-free Domain Adaptive Semantic Segmentation via Importance-aware and Prototype-contrast Learning
FACT: Federated Adversarial Cross Training
Source-Free Domain Adaptation for SSVEP-based Brain-Computer Interfaces
Source-Free Domain Adaptation for RGB-D Semantic Segmentation with Vision Transformers
Imbalance-Agnostic Source-Free Domain Adaptation via Avatar Prototype Alignment
Black-box Source-free Domain Adaptation via Two-stage Knowledge Distillation
CoSDA: Continual Source-Free Domain Adaptation
Source-free Domain Adaptation Requires Penalized Diversity
Few-shot Fine-tuning is All You Need for Source-free Domain Adaptation
Uncertainty-Aware Source-Free Adaptive Image Super-Resolution with Wavelet Augmentation Transformer
C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation
SFHarmony: Source Free Domain Adaptation for Distributed Neuroimaging Analysis
A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts
Spatio-Temporal Pixel-Level Contrastive Learning-based Source-Free Domain Adaptation for Video Semantic Segmentation
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