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Synthetic-to-Real Translation

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

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Papers

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Showing 31–40 of 68 papers

TitleStatusHype
Contrastive Learning and Self-Training for Unsupervised Domain Adaptation in Semantic Segmentation—0
Self-supervised Augmentation Consistency for Adapting Semantic SegmentationCode1
Domain Adaptive Semantic Segmentation with Self-Supervised Depth EstimationCode1
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene SegmentationCode1
Coarse-to-Fine Domain Adaptive Semantic Segmentation with Photometric Alignment and Category-Center Regularization—0
MetaCorrection: Domain-aware Meta Loss Correction for Unsupervised Domain Adaptation in Semantic SegmentationCode1
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic SegmentationCode1
cGANs for Cartoon to Real-life Images—0
Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic SegmentationCode1
Permuted AdaIN: Reducing the Bias Towards Global Statistics in Image ClassificationCode1
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