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Synthetic-to-Real Translation

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보존된 평가 기록: 119

Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.

<span style="color:grey; opacity: 0.6">( Image credit: CYCADA )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/andreasveit/densenet-pytorch

Deep Residual Learning for Image Recognition

https://github.com/andrijdavid/clinical-brain-interface-2020

Deep Residual Learning for Image Recognition

https://github.com/angelvillar96/FaceEmoji

Deep Residual Learning for Image Recognition

https://github.com/anguyen8/sam

Deep Residual Learning for Image Recognition

https://github.com/anibali/dsnt-pose2d

Deep Residual Learning for Image Recognition

https://github.com/aniket-patra-1998/FLOWER_RECOGNITION

Deep Residual Learning for Image Recognition

https://github.com/anjandeepsahni/face_classification

Deep Residual Learning for Image Recognition

https://github.com/anjandeepsahni/face_recognition

Deep Residual Learning for Image Recognition

https://github.com/ankit-vaghela30/Google-landmark-prediction

Deep Residual Learning for Image Recognition

https://github.com/anoushkrit/Knowledge

Deep Residual Learning for Image Recognition

https://github.com/antoinedemathelin/wann

Domain-Adversarial Training of Neural Networks

https://github.com/anuraggoel70/Domain-Adversarial-Training-of-Neural-Networks

Domain-Adversarial Training of Neural Networks

https://github.com/apple/ml-cvpr2019-swd

Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation

https://github.com/arahosu/musicslots

Deep Residual Learning for Image Recognition

https://github.com/ariangc/breinchallenge

Deep Residual Learning for Image Recognition

https://github.com/asahi417/DeepDomainAdaptation

Domain-Adversarial Training of Neural Networks

https://github.com/asbjrnmunk/mdd-unet

Bidirectional Learning for Domain Adaptation of Semantic Segmentation

https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras

Deep Residual Learning for Image Recognition

https://github.com/asprenger/keras_fc_densenet

Deep Residual Learning for Image Recognition

https://github.com/astoycos/Mini_Project2

Deep Residual Learning for Image Recognition

https://github.com/attm/tensorflow_advent

ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation

https://github.com/aurelien-peden/Deep-Learning-paper-implementations

Deep Residual Learning for Image Recognition

https://github.com/avocardio/resnet_vs_convnext

Deep Residual Learning for Image Recognition

https://github.com/axmoyal/Steganalysis-with-CNN

Deep Residual Learning for Image Recognition

https://github.com/b8goal/PIRL-Residual-network

Deep Residual Learning for Image Recognition

https://github.com/barmayo/spatial_attention

Deep Residual Learning for Image Recognition

https://github.com/bclwan/MRI_Brain_Segmentation

Deep Residual Learning for Image Recognition

https://github.com/ben-davidson-6/fixup

Deep Residual Learning for Image Recognition

https://github.com/bentrevett/pytorch-image-classification

Deep Residual Learning for Image Recognition

https://github.com/bertrandlalo/deep_dog_breeds

Deep Residual Learning for Image Recognition

https://github.com/bethgelab/InDomainGeneralizationBenchmark

Deep Residual Learning for Image Recognition

https://github.com/boris127/vehicle-detection

Deep Residual Learning for Image Recognition

https://github.com/brycexu/MR-Residual-Net

Deep Residual Learning for Image Recognition

https://github.com/buriedms/AdaptSegNet-Paddle

Learning to Adapt Structured Output Space for Semantic Segmentation

https://github.com/c1ph3rr/Deep-Residual-Learning-for-Image-Recognition

Deep Residual Learning for Image Recognition

https://github.com/calico/scnym

Domain-Adversarial Training of Neural Networks

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