Synthetic-to-real translation is the task of domain adaptation from synthetic (or virtual) data to real data.
( Image credit: CYCADA )
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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.
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.
Predicting structured outputs such as semantic segmentation relies on expensive per-pixel annotations to learn supervised models like convolutional neural networks.
In this paper, we propose an adversarial learning method for domain adaptation in the context of semantic segmentation.
Ranked #7 on Image-to-Image Translation on SYNTHIA-to-Cityscapes
Domain adaptation is critical for success in new, unseen environments.
Ranked #1 on Image-to-Image Translation on SYNTHIA Fall-to-Winter
We consider the problem of unsupervised domain adaptation in semantic segmentation.
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.
Hence, we propose a curriculum-style learning approach to minimizing the domain gap in urban scene semantic segmentation.
Ranked #14 on Image-to-Image Translation on SYNTHIA-to-Cityscapes
Hence, we propose a curriculum-style learning approach to minimize the domain gap in urban scenery semantic segmentation.
Ranked #15 on Image-to-Image Translation on SYNTHIA-to-Cityscapes
In this paper, we introduce the first domain adaptive semantic segmentation method, proposing an unsupervised adversarial approach to pixel prediction problems.
Ranked #2 on Image-to-Image Translation on SYNTHIA Fall-to-Winter