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
Learning to Adapt Structured Output Space for Semantic Segmentation
wasidennis/AdaptSegNet
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CVPR 2018
In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation.
Domain Adaptation for Structured Output via Discriminative Patch Representations
wasidennis/AdaptSegNet
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ICCV 2019
Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks.
Diverse Image-to-Image Translation via Disentangled Representations
HsinYingLee/DRIT
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ECCV 2018
Our model takes the encoded content features extracted from a given input and the attribute vectors sampled from the attribute space to produce diverse outputs at test time.
Virtual to Real Reinforcement Learning for Autonomous Driving
rahul263-stack/Self-Driving-Car
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To our knowledge, this is the first successful case of driving policy trained by reinforcement learning that can adapt to real world driving data.
ADVENT: Adversarial Entropy Minimization for Domain Adaptation in Semantic Segmentation
Semantic segmentation is a key problem for many computer vision tasks.
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
stu92054/Domain-adaptation-on-segmentation
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In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems.
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
jhoffman/cycada_release
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ICML 2018
Domain adaptation is critical for success in new, unseen environments.
Bidirectional Learning for Domain Adaptation of Semantic Segmentation
liyunsheng13/BDL
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CVPR 2019
In this paper, we propose a novel bidirectional learning framework for domain adaptation of segmentation.
Rectifying Pseudo Label Learning via Uncertainty Estimation for Domain Adaptive Semantic Segmentation
This paper focuses on the unsupervised domain adaptation of transferring the knowledge from the source domain to the target domain in the context of semantic segmentation.
DAFormer: Improving Network Architectures and Training Strategies for Domain-Adaptive Semantic Segmentation
It improves the state of the art by 10. 8 mIoU for GTA-to-Cityscapes and 5. 4 mIoU for Synthia-to-Cityscapes and enables learning even difficult classes such as train, bus, and truck well.
