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/se111/side
ProxyMix: Proxy-based Mixup Training with Label Refinery for Source-Free Domain Adaptation
https://github.com/seansoleyman/cifar10-resnet
https://github.com/sebsquire/Dogs-and-cats-image-classification-CNN
https://github.com/sebsquire/FlowersRecognition_keras
https://github.com/seishinkikuchi/test
https://github.com/seqam-lab/GPDA
Unsupervised Visual Domain Adaptation: A Deep Max-Margin Gaussian Process Approach
https://github.com/serre-lab/gala_tpu
https://github.com/seyrankhademi/ResNet_CIFAR10
https://github.com/sgrvinod/a-pytorch-tutorial-to-super-resolution
https://github.com/shallowlearn/sportsreid
https://github.com/shao-chi/ImageCaption
https://github.com/shijianjian/efficientnet-pytorch-3d
https://github.com/shoaibahmed/CrossLayerPooling
https://github.com/shoji9x9/CIFAR-10-By-small-ResNet
https://github.com/shoji9x9/Fashion-MNIST-By-ResNet
https://github.com/shtnkgm/VisionFrameworkSample
https://github.com/sidify/resnet_focal_loss
https://github.com/sigeek/mldl-rgbd-da
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
https://github.com/sigeek/rgbd-da-project
Unsupervised Domain Adaptation through Inter-modal Rotation for RGB-D Object Recognition
https://github.com/smc-x/ms-resnetv2
https://github.com/smkim7-kr/AdaMatch-pytorch
AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation
https://github.com/sobalgi/cuda
CUDA: Contradistinguisher for Unsupervised Domain Adaptation
https://github.com/sohyun-l/ExLPose
FIFO: Learning Fog-invariant Features for Foggy Scene Segmentation
https://github.com/souravverma94/pre_trained_imgclassifier_dogBreeds
https://github.com/sroutray/da-ganin
https://github.com/stone5265/3c-reid
Person Transfer GAN to Bridge Domain Gap for Person Re-Identification
https://github.com/suhas1999/Flip-kart-grid-challenge
https://github.com/sumitkutty/Anti-Spoof-Face-Recognition
https://github.com/sungyoonahn/Hint-based-image-colorization-using-Attention-Unet
https://github.com/sunlizhuang/YOLOv1-PaddlePaddle
https://github.com/sunnysoni97/single_image_fl
https://github.com/swasun/VQ-VAE-images
https://github.com/swetak20/Deep_into_CNNs
https://github.com/syiin/ann_cancer_detection
https://github.com/syiin/resnet_cancer_detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.