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
Latest papers
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.
Learning from Scale-Invariant Examples for Domain Adaptation in Semantic Segmentation
MNaseerSubhani/LSE
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ECCV 2020
Specifically, we show that semantic segmentation model produces output with high entropy when presented with scaled-up patches of target domain, in comparison to when presented original size images.
Classes Matter: A Fine-grained Adversarial Approach to Cross-domain Semantic Segmentation
To fully exploit the supervision in the source domain, we propose a fine-grained adversarial learning strategy for class-level feature alignment while preserving the internal structure of semantics across domains.
DACS: Domain Adaptation via Cross-domain Mixed Sampling
vikolss/DACS
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In this paper we address the problem of unsupervised domain adaptation (UDA), which attempts to train on labelled data from one domain (source domain), and simultaneously learn from unlabelled data in the domain of interest (target domain).
Unsupervised Intra-domain Adaptation for Semantic Segmentation through Self-Supervision
feipan664/IntraDA
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CVPR 2020
Finally, to decrease the intra-domain gap, we propose to employ a self-supervised adaptation technique from the easy to the hard split.
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.
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.
Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation
RogerZhangzz/CAG_UDA
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NeurIPS 2019
Although there has been a progress in matching the marginal distributions between two domains, the classifier favors the source domain features and makes incorrect predictions on the target domain due to category-agnostic feature alignment.
MLSL: Multi-Level Self-Supervised Learning for Domain Adaptation with Spatially Independent and Semantically Consistent Labeling
Thus helping latent space learn the representation even when there are very few pixels belonging to the domain category (small object for example) compared to rest of the image.
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.
