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/jswati31/cuda-ghr
공식 코드CUDA-GHR: Controllable Unsupervised Domain Adaptation for Gaze and Head Redirection
https://github.com/juliangebele/CARLANE
공식 코드https://github.com/junkataoka/avatar
공식 코드AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain
https://github.com/justinkay/aldi
공식 코드Align and Distill: Unifying and Improving Domain Adaptive Object Detection
https://github.com/jxhuang0508/CaCo
공식 코드Category Contrast for Unsupervised Domain Adaptation in Visual Tasks
https://github.com/jxhuang0508/CrCDA
공식 코드Contextual-Relation Consistent Domain Adaptation for Semantic Segmentation
https://github.com/kaiizhang/mfa
공식 코드Multiple Fusion Adaptation: A Strong Framework for Unsupervised Semantic Segmentation Adaptation
https://github.com/khu-agi/bus
공식 코드https://github.com/kiananvari/statistical-distance-guided-unsupervised-domain-adaptation-for-automated-multi-class-cmr-iqa
공식 코드https://github.com/korawat-tanwisuth/proto_da
공식 코드A Prototype-Oriented Framework for Unsupervised Domain Adaptation
https://github.com/koutilya40192/SharinGAN
공식 코드SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry Estimation
https://github.com/kowshikthopalli/cattan
공식 코드https://github.com/kpeng9510/featfsda
공식 코드Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains
https://github.com/kpeng9510/relamix
공식 코드Exploring Few-Shot Adaptation for Activity Recognition on Diverse Domains
https://github.com/kw01sg/crda
공식 코드https://github.com/lanmng/roadda
공식 코드https://github.com/layumi/AdaBoost_Seg
공식 코드Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
https://github.com/layumi/Cifar10-Adaboost
공식 코드Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
https://github.com/layumi/Seg-Uncertainty
공식 코드Unsupervised Scene Adaptation with Memory Regularization in vivo
https://github.com/layumi/person-reid-3d
공식 코드Parameter-Efficient Person Re-identification in the 3D Space
https://github.com/lbktrinh/DRU
공식 코드Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection
https://github.com/lccurious/DRDA
공식 코드https://github.com/ldkong1205/conda
공식 코드ConDA: Unsupervised Domain Adaptation for LiDAR Segmentation via Regularized Domain Concatenation
https://github.com/lhaof/asc
공식 코드https://github.com/lhoyer/DAFormer
공식 코드https://github.com/lhoyer/hrda
공식 코드HRDA: Context-Aware High-Resolution Domain-Adaptive Semantic Segmentation
https://github.com/lhoyer/mic
공식 코드MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation
https://github.com/li-xirong/ude
공식 코드https://github.com/liamheng/annotation-free-medical-image-enhancement
공식 코드https://github.com/light-47/ida
공식 코드Intermediate Domain-guided Adaptation for Unsupervised Chorioallantoic Membrane Vessel Segmentation
https://github.com/lijin118/ATM
공식 코드https://github.com/lijin118/CGDM
공식 코드Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation
https://github.com/lijuntaopku/UFD
공식 코드Unsupervised Domain Adaptation of a Pretrained Cross-Lingual Language Model
https://github.com/lingrayy/plpb
공식 코드Robust Source-Free Domain Adaptation for Fundus Image Segmentation
https://github.com/links-ads/igarss-fmars
공식 코드FMARS: Annotating Remote Sensing Images for Disaster Management using Foundation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.