Papers Unsupervised Domain Adaptation
“Unsupervised Domain Adaptation” 태그가 달린 논문 2,104편 · 필터 해제
Unsupervised Domain Adaptation for Enhanced Radiometer Image Precipitation Estimation using Conditional Flow Matching
Deep generative networks have recently achieved unprecedented performance in precise image and video editing using sophisticated textual prompts. However, the effectiveness of such models heavily depends on access to ver…
Unsupervised Domain AdaptationSimFuse3D: Source-Guided Target Simulation and Confidence-Guided Multi-Stage Localization Reweighting for Cross-Platform 3D Object Detection
Changes in sensor height and viewpoint alter object-level point distributions, making cross-platform LiDAR unsupervised domain adaptation (UDA) difficult. Self-training uses labeled source scans and unlabeled target scan…
Unsupervised Domain Adaptation3D Object DetectionUnsupervised Adaptation of 3D CT Foundation Models for 3D CBCT Segmentation
Accurate 3D segmentation of cone-beam CT (CBCT) is critical for interventional and radiation therapy applications, yet it remains limited by two compounding challenges: the scarcity of annotated CBCT data and the large d…
Unsupervised Domain AdaptationLiver SegmentationAgainst Political Polarization: A Unified Framework for Tracing Evolving Political Ideologies on Social Media
The rapid growth of social media has greatly influenced political discourse, highlighting the need to understand individual political ideologies and their temporal dynamics. This task faces challenges such as data scarci…
Unsupervised Domain AdaptationStyle TransferUltra: Unsupervised Cross-Task Optimization for Reliable Restoration Segmentation Collaboration under Adverse Weather
Unsupervised Domain Adaptation for Adverse Weather Semantic Segmentation (UDA-ASS) aims to transfer semantic knowledge from labeled normal-weather images to unlabeled adverse environments. Existing approaches implicitly …
Unsupervised Domain AdaptationSemantic SegmentationObject DetectionYOLOv14: Adaptive Real-Time Object Detection for Diverse Imaging Conditions
Real-time object detectors achieve remarkable accuracy under controlled conditions, yet degrade sharply on non-ideal inputs-fisheye distortion, game-rendered content, aerial views, and 360°panoramas. We present YOLOv14, …
Unsupervised Domain AdaptationReal-Time Object DetectionTowards Practical Algorithm Selection for Unsupervised Domain Adaptation in Medical Imaging
Numerous unsupervised domain adaptation (UDA) algorithms exist, but for clinical practice, selecting the best-suited one along with proper hyperparameters often remains unclear, as the unlabeled deployment (target) domai…
Unsupervised Domain AdaptationVariance-reduced Domain Adaptation using Paired Sampling
Correlation alignment and the maximum mean discrepancy are two widely used distribution-matching frameworks for unsupervised domain adaptation (UDA). However, high variance in these losses has been shown to undermine the…
Unsupervised Domain AdaptationUnsupervised Domain Adaptation for Calcification Classification in Mammography Across Multi-Site Datasets
Deep learning-based computer-aided diagnosis (CAD) systems have shown strong performance in breast cancer diagnosis, particularly for classification tasks in mammography. However, domain shifts across multi-site datasets…
Unsupervised Domain AdaptationStyle TransferSNR-Adaptive Unified Diffusion for Multi-Task Medical Image Segmentation
Clinical cardiac imaging pipelines currently deploy separate models for each dataset and modality, incurring redundant training costs and precluding knowledge sharing across anatomically related tasks. Consolidating semi…
Unsupervised Domain AdaptationMedical Image SegmentationA Step Towards Robust Unsupervised Domain Adaptation via Fine-Tuning and Reinforcement Learning
Adversarial robustness in Unsupervised Domain Adaptation (UDA) remains a significant challenge due to noisy pseudo labels and inherent distributional shifts between the clean source and adversarially perturbed target dom…
Unsupervised Domain AdaptationReinforcement LearningAdversarial RobustnessPGUDA: Pressure-Guided Unsupervised Domain Adaptation with Cross-Modal Knowledge Distillation for sEMG-Based Gesture Recognition
Surface electromyography (sEMG)-based gesture recognition has emerged as a promising technology for natural human-computer interaction. However, its practical deployment remains challenging due to severe performance degr…
Unsupervised Domain AdaptationRepresentation LearningKnowledge DistillationGesture RecognitionDomain Adaptive Object Detection via Dual-Stream Bilevel-Cycle Optimization
Cycle self-training (CST) breaks the shared classifier assumption of the standard self-training framework, which is effective for unsupervised domain adaptation and exploits unlabeled target data by training with target …
Unsupervised Domain AdaptationImage ClassificationObject DetectionA cross-process welding penetration status prediction algorithm based on unsupervised domain adaptation in laser and TIG welding
Supervised deep learning has been widely used for weld penetration state classification; however, its performance often degrades significantly under domain shift, such as when transferring models between welding processe…
Unsupervised Domain AdaptationDomain Adaptation Under Wireless Network Constraints: When Does It Become Green?
The deployment of data-driven models in 6G wireless networks is increasingly challenged by frequent distribution shifts that degrade performance over time. Unsupervised Domain Adaptation (UDA) offers an alternative appro…
Unsupervised Domain AdaptationConvergence Analysis of Nyström Subsampling in Covariate Shift Adaptation for Misspecified case
This paper investigates convergence properties of regularized Nyström subsampling applied to the unsupervised domain adaptation problem under covariate shift. We focus on the low-smoothness (misspecified) case where the …
Unsupervised Domain AdaptationMixedPEFT: Combining Multiple PEFT Methods with Mixed Objectives for Unsupervised Domain Adaptation
Pre-trained language models struggle when applied to new domains, as full fine-tuning is computationally expensive and prone to catastrophic forgetting. This study addresses this challenge by presenting a novel parameter…
Unsupervised Domain AdaptationNatural Language InferenceUnsupervised Domain Adaptation for Sim-to-Real Object Pose Estimation with Contrastive Alignment and Pseudo-Label Refinement
Unsupervised domain adaptation (UDA) enables robust transfer of knowledge from simulated to real environments while exploiting a subset of unlabeled target data to improve real-world performance. Existing UDA methods for…
Unsupervised Domain AdaptationPose EstimationOne Size does not Fit All: Heterogeneous Latent Space Alignment for Unsupervised Domain Adaptation
Domain shift remains a major obstacle to the reliable deployment of machine learning models in high-stakes environments such as healthcare. While Domain adaptation aims to mitigate these effects, existing approaches suff…
Unsupervised Domain AdaptationMedical Image SegmentationVT-DUDA: Visual Token Conditioning for Diffusion-guided Unsupervised Domain Adaptation
Unsupervised domain adaptation (UDA) aims to learn a target-domain classifier from labeled source data and unlabeled target data under distribution shift. Recent diffusion-based UDA methods approach this problem by synth…
Unsupervised Domain Adaptation