Papers with Code 아카이브
Synthetic-to-Real Translation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
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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/yangorwell/NGPlus
https://github.com/yangyucheng000/resnet_Ascend
https://github.com/yashclone999/ResNet_MODEL
https://github.com/ycao5602/SAL
https://github.com/yibeiou/Advanced_Business_Intelligence
https://github.com/yoshitomo-matsubara/torchdistill
https://github.com/yuan-zm/dgt-st
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/yudhisteer/Face-Recognition-with-Masks
https://github.com/yw0nam/DenseNet
https://github.com/yxgeee/MMT
https://github.com/zjZSTU/ResNet
https://github.com/zqwhu/SegDAwithBoundary
Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/zsano1/MEnet-FCN-wild
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
https://gitlab.com/birder/birder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.