Papers with Code 아카이브
Synthetic-to-Real Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 4
- 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/Punakshi/Deep-Residual-Learning-for-Image-Recognition-Implementation
https://github.com/Rakshit-Shetty/Resnet-Implementation
https://github.com/Raykoooo/IAST
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
https://github.com/RobotMobile/cv-deep-learning-paper-review
https://github.com/Rohed/ml-1
https://github.com/SANKHA1/Vehicle-Detection
https://github.com/SJTU-Thinklab-Det/r3det-on-mmdetection
https://github.com/Sakib1263/1DResNet-Builder-KERAS
https://github.com/Sakib1263/1DResNet-KERAS
https://github.com/Sakib1263/DenseNet-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-Model-Builder-KERAS
https://github.com/Sakib1263/ResNet-Model-Builder-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet-ResNetv2-ResNeXt-1D-2D-Tensorflow-Keras
https://github.com/Sakib1263/ResNet1D-Model-Builder-KERAS
https://github.com/Sangkwun/Resnet
https://github.com/SarthakGarg13/OTOMYCOSIS
https://github.com/SethEBaldwin/Resnet
https://github.com/Seung-Hun-Lee/ADAS
https://github.com/ShichengChen/Domain-Adversarial-Training-of-Neural-Networks
https://github.com/ShichengChen/WaveNetSeparateAudio
https://github.com/ShigemichiMatsuzaki/MSPL
https://github.com/SiHaoShen/Convolutional-Neural-Networks
https://github.com/Solacex/CCM
https://github.com/Sshanu/AdaptSegNet
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/Sudhir11292rt/myBDL
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
https://github.com/ThanasisMattas/smartflow
https://github.com/Thehunk1206/Covid-19-chest-X-ray
https://github.com/Thehunk1206/Covid-19-covidcnn
https://github.com/TowardHumanizedInteraction/TripletFace
https://github.com/UnofficialJuliaMirror/DeepMark-deepmark
https://github.com/UnofficialJuliaMirrorSnapshots/DeepMark-deepmark
https://github.com/Vamshi399/CarND-Vehicle-Detection
https://github.com/VamsiKrishna1211/gender_age-detection
https://github.com/VhalPurohit/290s
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.