Synthetic-to-Real Translation
55 papers with code • 4 benchmarks • 5 datasets
Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.
( Image credit: CYCADA )
Benchmarks
These leaderboards are used to track progress in Synthetic-to-Real Translation
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Libraries
Use these libraries to find Synthetic-to-Real Translation models and implementations
SYNTHIA
GTA5
Syn2Real
Sims4Action
WinSyn
Most implemented papers
A Curriculum Domain Adaptation Approach to the Semantic Segmentation of Urban Scenes
YangZhang4065/AdaptationSeg
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Hence, we propose a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation.
Confidence Regularized Self-Training
yzou2/CRST
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ICCV 2019
Recent advances in domain adaptation show that deep self-training presents a powerful means for unsupervised domain adaptation.
Unsupervised Scene Adaptation with Memory Regularization in vivo
We consider the unsupervised scene adaptation problem of learning from both labeled source data and unlabeled target data.
Instance Adaptive Self-Training for Unsupervised Domain Adaptation
bupt-ai-cz/IAST-ECCV2020
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ECCV 2020
In this paper, we propose an instance adaptive self-training framework for UDA on the task of semantic segmentation.
Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation
In this paper, we rely on representative prototypes, the feature centroids of classes, to address the two issues for unsupervised domain adaptation.
Adaptive Boosting for Domain Adaptation: Towards Robust Predictions in Scene Segmentation
Domain adaptation is to transfer the shared knowledge learned from the source domain to a new environment, i. e., target domain.
Curriculum Domain Adaptation for Semantic Segmentation of Urban Scenes
YangZhang4065/AdaptationSeg
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ICCV 2017
Hence, we propose a curriculum-style learning approach to minimize the domain gap in urban scenery semantic segmentation.
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
We consider the problem of unsupervised domain adaptation in semantic segmentation.
Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
yzou2/CBST
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In this paper, we propose a novel UDA framework based on an iterative self-training procedure, where the problem is formulated as latent variable loss minimization, and can be solved by alternatively generating pseudo labels on target data and re-training the model with these labels.
All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation
a514514772/DISE-Domain-Invariant-Structure-Extraction
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CVPR 2019
In this paper we tackle the problem of unsupervised domain adaptation for the task of semantic segmentation, where we attempt to transfer the knowledge learned upon synthetic datasets with ground-truth labels to real-world images without any annotation.
