https://github.com/vturrisi/udavt
공식 코드Unsupervised Domain Adaptation for Video Transformers in Action Recognition
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Unsupervised Domain Adaptation for Video Transformers in Action Recognition
Manifold Criterion Guided Transfer Learning via Intermediate Domain Generation
Syn-to-Real Unsupervised Domain Adaptation for Indoor 3D Object Detection
Synthetic-to-Real Unsupervised Domain Adaptation for Scene Text Detection in the Wild
BiPC: Bidirectional Probability Calibration for Unsupervised Domain Adaption
Improve Cross-domain Mixed Sampling with Guidance Training for Adaptive Segmentation
Unsupervised Structure-Texture Separation Network for Oracle Character Recognition
Unsupervised Reinforcement Adaptation for Class-Imbalanced Text Classification
Robust Unsupervised Domain Adaptation for Neural Networks via Moment Alignment
Weakly-Supervised Cross-Domain Segmentation of Electron Microscopy with Sparse Point Annotation
Enabling Country-Scale Land Cover Mapping with Meter-Resolution Satellite Imagery
Mimic Embedding via Adaptive Aggregation: Learning Generalizable Person Re-identification
Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation
Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation
PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds
Unsupervised Modality Adaptation with Text-to-Image Diffusion Models for Semantic Segmentation
Quantum correlation alignment for unsupervised domain adaptation
Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency
Domain Generalization of 3D Object Detection by Density-Resampling
Towards Generic Semi-Supervised Framework for Volumetric Medical Image Segmentation
GraphEcho: Graph-Driven Unsupervised Domain Adaptation for Echocardiogram Video Segmentation
Context-Aware Pseudo-Label Refinement for Source-Free Domain Adaptive Fundus Image Segmentation
Addressing Parameter Choice Issues in Unsupervised Domain Adaptation by Aggregation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.