Papers with Code 아카이브
Synthetic-to-Real Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/ViswanathaReddyGajjala/EfficientNet-RetinaNet
https://github.com/Wanger-SJTU/FCN-in-the-wild
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
https://github.com/Wenchao-Du/LIR-for-Unsupervised-IR
Diverse Image-to-Image Translation via Disentangled Representations
https://github.com/YudeWang/UNet-Satellite-Image-Segmentation
https://github.com/Zachdr1/CNNs
https://github.com/abaietto/neural_ode_classification
https://github.com/abuchin/resnet_sign
https://github.com/aby2s/sharpmask
https://github.com/adapt-python/adapt
Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/adldotori/ResNet
https://github.com/ajaystar8/PDRUNet-PyTorch
https://github.com/ajithvallabai/getsetgo_keras-beginner
https://github.com/akamaster/pytorch_resnet_cifar10
https://github.com/akar5h/Image-Recognition-with-CNN-and-Transfer-Learning-ResNet101
https://github.com/akhadangi/EM-net
https://github.com/akommini/Spoken-Numeric-Digit-detection
https://github.com/alalagong/LEDNet
https://github.com/alecwangcq/KFAC-Pytorch
https://github.com/alex-f1tor/Image-Caption
https://github.com/alexdominguez09/pedestrian_direction
Virtual to Real Reinforcement Learning for Autonomous Driving
https://github.com/alexsax/midlevel-reps
https://github.com/alexwdong/IncubatorCVProject
https://github.com/alibaba/EasyCV
https://github.com/alililia/ascend_FaceAttribute
https://github.com/alililia/ascend_SE-Net
https://github.com/alililia/ms_extend/tree/main/gpu_resnet
https://github.com/alisha17/benchmarks
https://github.com/alrojo/lasagne_residual_network
https://github.com/alvarobartt/serving-pytorch-models
https://github.com/aman-garg0001/fruit-classifier
https://github.com/amazon-research/long-short-term-transformer
https://github.com/amazon-science/long-short-term-transformer
https://github.com/amogh7joshi/engagement-detection
https://github.com/amogh7joshi/fer
https://github.com/amogh7joshi/plant-health-detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.