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/MiX-S/resnet_cifar10
https://github.com/MichaelWangfc/distributed-tensorflow-resnet
https://github.com/Mind23-2/MindCode-101/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-3/tree/main/lresnet100e_ir
https://github.com/Mind23-2/MindCode-67
https://github.com/Mind23-2/MindCode-70
https://github.com/MindCode-4/code-10/tree/main/Alexnet-ABeffect
https://github.com/MindCode-4/code-4/tree/main/rembert
https://github.com/MindSpore-paper-code-2/code3/tree/main/ssd_resnet34
https://github.com/MindSpore-paper-code-3/code3/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code5/tree/main/resnet50_adv_pruning
https://github.com/MindSpore-paper-code-3/code6/tree/main/Alexnet
https://github.com/MindSpore-paper-code-3/code7/tree/main/FaceAttribute
https://github.com/MindSpore-paper-code-3/code8/tree/main/DepthNet
S2R-DepthNet: Learning a Generalizable Depth-specific Structural Representation
https://github.com/MindSpore-paper-code-3/code8/tree/main/FaceAttribute
https://github.com/MioChiu/ResNet_TensorFlow
https://github.com/MishaLaskin/vq-vae
https://github.com/MishaLaskin/vqvae
https://github.com/MouradElHamri/HOT-DA
Hierarchical Optimal Transport for Unsupervised Domain Adaptation
https://github.com/MrRiahi/Convolutional-Neural-Networks-Tensorflow
https://github.com/NASA-NeMO-Net/NeMO-Net
https://github.com/NVIDIA/retinanet-examples
https://github.com/NVlabs/DG-Net
Joint Discriminative and Generative Learning for Person Re-identification
https://github.com/Nadavc220/DomainAdversarialTrainingOfNeuralNetworks
https://github.com/NazaninMn/GenGMM
GenGMM: Generalized Gaussian-Mixture-based Domain Adaptation Model for Semantic Segmentation
https://github.com/Neverland610/MSUDA_REID
Unsupervised Multi-Source Domain Adaptation for Person Re-Identification
https://github.com/Nyn-ynu/MCD
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/OlafenwaMoses/Action-Net
https://github.com/OlafenwaMoses/IdenProf
https://github.com/OlafenwaMoses/Traffic-Net
https://github.com/Olayemiy/Image-Classification-With-Resnet
https://github.com/PPPrior/i3d-pytorch
https://github.com/PRIS-CV/Mutual-to-Separate
Mind the Gap: Enlarging the Domain Gap in Open Set Domain Adaptation
https://github.com/PaddlePaddle/PaddleClas
https://github.com/PaddlePaddle/PaddleDetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.