https://github.com/lukemelas/pixmatch
공식 코드PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency Training
Papers with Code 아카이브
Synthetic-to-Real Translation
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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/luyvlei/BiSMAPs
공식 코드https://github.com/microsoft/ProDA
공식 코드https://github.com/mil-tokyo/MCD_DA
공식 코드Maximum Classifier Discrepancy for Unsupervised Domain Adaptation
https://github.com/onuriel/PermutedAdaIN
공식 코드Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image Classification
https://github.com/qianyuzqy/CAMix
공식 코드Context-Aware Mixup for Domain Adaptive Semantic Segmentation
https://github.com/qianyuzqy/RCCR
공식 코드Domain Adaptive Semantic Segmentation via Regional Contrastive Consistency Regularization
https://github.com/qinenergy/corda
공식 코드Domain Adaptive Semantic Segmentation with Self-Supervised Depth Estimation
https://github.com/royee182/dpl
공식 코드Dual Path Learning for Domain Adaptation of Semantic Segmentation
https://github.com/shahaf1313/procst
공식 코드ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer
https://github.com/user0407/CLUDA
공식 코드CLUDA : Contrastive Learning in Unsupervised Domain Adaptation for Semantic Segmentation
https://github.com/valeoai/ADVENT
공식 코드ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
https://github.com/vikolss/DACS
공식 코드https://github.com/visinf/da-sac
공식 코드Self-supervised Augmentation Consistency for Adapting Semantic Segmentation
https://github.com/wasidennis/AdaptSegNet
공식 코드Learning to Adapt Structured Output Space for Semantic Segmentation
https://github.com/xiaoachen98/DDB
공식 코드Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation
https://github.com/yeshaokai/Calibrator-Domain-Adaptation
공식 코드Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
https://github.com/yueatsprograms/uda_release
공식 코드https://github.com/yzou2/CBST
공식 코드Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
https://github.com/yzou2/CRST
공식 코드https://github.com/2023-MindSpore-1/ms-code-177
https://github.com/2023-MindSpore-1/ms-code-18/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-1/ms-code-201
https://github.com/2023-MindSpore-1/ms-code-208
https://github.com/2023-MindSpore-1/ms-code-213/tree/main/FaceAttribute
https://github.com/2023-MindSpore-1/ms-code-216/tree/main/resnet50_adv_pruning
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-1/ms-code-22
https://github.com/2023-MindSpore-4/Code-5/tree/main/lresnet100e_ir
https://github.com/2023-MindSpore-4/Code10/tree/main/ResNet
https://github.com/2023-MindSpore-4/Code11/tree/main/resnet50_adv_pruning
https://github.com/2023-MindSpore-4/Code11/tree/main/ssc_resnet50
https://github.com/2023-MindSpore-4/Code11/tree/main/ssd_resnet_34
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet34
https://github.com/2023-MindSpore-4/Code7/tree/main/ssd_resnet_34
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.