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Unsupervised Domain Adaptation
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Unsupervised Domain Adaptation is a learning framework to transfer knowledge learned from source domains with a large number of annotated training examples to target domains with unlabeled data only.
Source: Domain-Specific Batch Normalization for Unsupervised Domain Adaptation
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/RPIDIAL/SAMix
공식 코드Spectral Adversarial MixUp for Few-Shot Unsupervised Domain Adaptation
https://github.com/RS-CSU/MemoryAdaptNet-master
공식 코드https://github.com/ReNginx/ADeLA
공식 코드ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation
https://github.com/Rh-YE/ai4galmorph_desi
공식 코드https://github.com/RogerZhangzz/CAG_UDA
공식 코드Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
https://github.com/RoyalVane/ASM
공식 코드Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation
https://github.com/RoyalVane/CLAN
공식 코드https://github.com/RuiShu/dirt-t
공식 코드https://github.com/Rxannro/SWL-Adapt
공식 코드https://github.com/SCUT-AILab/CPGA
공식 코드Source-free Domain Adaptation via Avatar Prototype Generation and Adaptation
https://github.com/SHI-Labs/Unsupervised-Domain-Adaptation-with-Differential-Treatment
공식 코드https://github.com/Shawey94/WACV2025-FFTAT
공식 코드Feature Fusion Transferability Aware Transformer for Unsupervised Domain Adaptation
https://github.com/SiyuanHuang95/SUG
공식 코드SUG: Single-dataset Unified Generalization for 3D Point Cloud Classification
https://github.com/ThomasWestfechtel/SWG
공식 코드https://github.com/TingHLiao/ELDA
공식 코드ELDA: Using Edges to Have an Edge on Semantic Segmentation Based UDA
https://github.com/VMarsocci/geomultitasknet
공식 코드GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates
https://github.com/Vanint/CoUDA
공식 코드Collaborative Unsupervised Domain Adaptation for Medical Image Diagnosis
https://github.com/ViLab-UCSD/MemSAC_ECCV2022
공식 코드MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptation
https://github.com/VisionLearningGroup/CORAL
공식 코드https://github.com/VisionLearningGroup/DANCE
공식 코드https://github.com/VisionLearningGroup/taskcv-2017-public
공식 코드https://github.com/WangWenhao0716/Attentive-WaveBlock
공식 코드https://github.com/WangWenhao0716/DomainMix
공식 코드DomainMix: Learning Generalizable Person Re-Identification Without Human Annotations
https://github.com/WassimJabz/RecogniChess
공식 코드Unsupervised Domain Adaptation Approaches for Chessboard Recognition
https://github.com/Wenlve-Zhou/VLP-UDA
공식 코드Unsupervised Domain Adaption Harnessing Vision-Language Pre-training
https://github.com/YBZh/Label-Propagation-with-Augmented-Anchors
공식 코드https://github.com/YXSong000/UAD
공식 코드Multi-source-free Domain Adaptation via Uncertainty-aware Adaptive Distillation
https://github.com/YYinn/SFDA-DDFP
공식 코드https://github.com/YanchaoYang/FDA
공식 코드https://github.com/Yiyang98/BFUDA
공식 코드Clarinet: A One-step Approach Towards Budget-friendly Unsupervised Domain Adaptation
https://github.com/YoushanZhang/Transfer-Learning
공식 코드Deep Least Squares Alignment for Unsupervised Domain Adaptation
https://github.com/YoushanZhang/Transfer-Learning/tree/main/Code/Deep/PRPL
공식 코드Efficient Pre-trained Features and Recurrent Pseudo-Labeling in Unsupervised Domain Adaptation
https://github.com/YufanHe/self-domain-adapted-network
공식 코드https://github.com/ZHKKKe/DualStudent
공식 코드Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning
https://github.com/ZJULearning/MaxSquareLoss
공식 코드Domain Adaptation for Semantic Segmentation with Maximum Squares Loss
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.