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/hustvl/sparseinst
https://github.com/hwk0702/keras2torch/tree/main/Computer_Vision/Model_Soup
https://github.com/hx19940102/Image-Segmentation-based-on-FCN
https://github.com/hycis/TensorGraph
https://github.com/hyeonseobnam/sagnet
https://github.com/hyeonseobnam/style-agnostic-networks
https://github.com/hysts/pytorch_resnet
https://github.com/hysts/pytorch_resnet_preact
https://github.com/hz2538/iot_JennyGo
https://github.com/iArunava/ResNet
https://github.com/iiyama-lab/PS-VAEs
Partially-Shared Variational Auto-encoders for Unsupervised Domain Adaptation with Target Shift
https://github.com/imatif17/Joint-Unsupervised-Domain-adaption-and-Knowledge-distillation
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
https://github.com/imkoushik22/Walmart-Hackathon
https://github.com/intelligentMachines-ITU/Blood-Cancer-Dataset-Lukemia-Attri-MICCAI-2024
https://github.com/interestingzhuo/foodretreival
https://github.com/isee-laboratory/pixelfade
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/isheunesutembo/TB-Computer-Aided-Diagnosis-Using-Deep-Learning
https://github.com/ispc-lab/bmd
https://github.com/ispc-lab/glc-plus
https://github.com/itijyou/ademxapp
https://github.com/ivychill/reid
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/j3soon/arxiv-utils
https://github.com/jacobtyo/mudd
Learning Generalisable Omni-Scale Representations for Person Re-Identification
https://github.com/jameswang287/Car-Detection
https://github.com/janfreyberg/pytorch-revgrad
https://github.com/jdauba/Domain-Adaptation-SVHN-MNIST
Generative Pseudo-label Refinement for Unsupervised Domain Adaptation
https://github.com/jday96314/Kuzushiji
https://github.com/jeremyzhao1998/mrt-release
https://github.com/jhonve/implicitpcda
Domain Adaptation on Point Clouds via Geometry-Aware Implicits
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jiangsikai/Person_reID_baseline_pytorch
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/jimheaton/Ultra96_ML_Embedded_Workshop
https://github.com/jinlong17/da-detect
Domain Adaptive Faster R-CNN for Object Detection in the Wild
https://github.com/jiweibo/imagenet
https://github.com/jjessyli/speciteller
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.