Papers with Code 아카이브
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/xudonmao/RegCGAN
공식 코드Unpaired Multi-Domain Image Generation via Regularized Conditional GANs
https://github.com/xudonmao/VMT
공식 코드https://github.com/xuyu0010/atcon
공식 코드Source-free Video Domain Adaptation by Learning Temporal Consistency for Action Recognition
https://github.com/xuyu0010/awesome-video-domain-adaptation
공식 코드Video Unsupervised Domain Adaptation with Deep Learning: A Comprehensive Survey
https://github.com/xuzhez/mapseg
공식 코드https://github.com/yanan-wang-cs/clonedperson
공식 코드Cloning Outfits from Real-World Images to 3D Characters for Generalizable Person Re-Identification
https://github.com/yankeegsj/bla
공식 코드https://github.com/yaoyueduzhen/DADA
공식 코드https://github.com/yashsharma/mani
공식 코드MaNi: Maximizing Mutual Information for Nuclei Cross-Domain Unsupervised Segmentation
https://github.com/yasserben/datum
공식 코드One-shot Unsupervised Domain Adaptation with Personalized Diffusion Models
https://github.com/yeshaokai/Calibrator-Domain-Adaptation
공식 코드Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
https://github.com/yhldhit/WMMD-Caffe
공식 코드Mind the Class Weight Bias: Weighted Maximum Mean Discrepancy for Unsupervised Domain Adaptation
https://github.com/yifei-he/goat
공식 코드https://github.com/yihong-97/source-free-iapc
공식 코드https://github.com/yihong-97/source-free_iapc
공식 코드https://github.com/yihong-97/unlock
공식 코드Unlocking Constraints: Source-Free Occlusion-Aware Seamless Segmentation
https://github.com/yinjunbo/ssda3d
공식 코드SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud
https://github.com/yonghaoxu/ut-kd
공식 코드Multi-Target Unsupervised Domain Adaptation for Semantic Segmentation without External Data
https://github.com/ysxcj-hub/cca-lsc
공식 코드Contrastive Conditional Alignment based on Label Shift Calibration for Imbalanced Domain Adaptation
https://github.com/yue-zhongqi/icon
공식 코드Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation
https://github.com/yue-zhongqi/tcm
공식 코드Transporting Causal Mechanisms for Unsupervised Domain Adaptation
https://github.com/yueatsprograms/uda_release
공식 코드https://github.com/yuhed/proxymix
공식 코드ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation
https://github.com/yuhuayc/da-faster-rcnn
공식 코드Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/yukilulu/cac
공식 코드Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation
https://github.com/yuntaodu/CDEM
공식 코드Cross-domain error minimization for unsupervised domain adaptation
https://github.com/yurongyou/rote-da
공식 코드Unsupervised Adaptation from Repeated Traversals for Autonomous Driving
https://github.com/yvonneywu/udama
공식 코드https://github.com/ywher/TUFL
공식 코드Threshold-adaptive Unsupervised Focal Loss for Domain Adaptation of Semantic Segmentation
https://github.com/ywher/sam4udass
공식 코드SAM4UDASS: When SAM Meets Unsupervised Domain Adaptive Semantic Segmentation in Intelligent Vehicles
https://github.com/ywzcode/CS-adv
공식 코드https://github.com/yxgeee/MMT
공식 코드https://github.com/yxgeee/SDA
공식 코드Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
https://github.com/yxgeee/SpCL
공식 코드Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID
https://github.com/yxgeee/VisDA-ECCV20
공식 코드Structured Domain Adaptation with Online Relation Regularization for Unsupervised Person Re-ID
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.