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Domain Stylization: A Strong, Simple Baseline for Synthetic to Real Image Domain Adaptation

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arxiv 1807.09384 v1 pith:MKVR75AY submitted 2018-07-24 cs.CV cs.LG

Aysegul Dundar , Ming-Yu Liu , Ting-Chun Wang , John Zedlewski , Jan Kautz This is my paper · ORCID
classification cs.CVcs.LG
keywords domainsynthetic-to-realadaptationalgorithmapproachdetectiondistanceimage
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Deep neural networks have largely failed to effectively utilize synthetic data when applied to real images due to the covariate shift problem. In this paper, we show that by applying a straightforward modification to an existing photorealistic style transfer algorithm, we achieve state-of-the-art synthetic-to-real domain adaptation results. We conduct extensive experimental validations on four synthetic-to-real tasks for semantic segmentation and object detection, and show that our approach exceeds the performance of any current state-of-the-art GAN-based image translation approach as measured by segmentation and object detection metrics. Furthermore we offer a distance based analysis of our method which shows a dramatic reduction in Frechet Inception distance between the source and target domains, offering a quantitative metric that demonstrates the effectiveness of our algorithm in bridging the synthetic-to-real gap.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation

    cs.CV 2019-09 conditional novelty 6.0 of 10

    A cycle-free target-guided GAN that transfers source images into target style, combined with self-ensembling, achieves state-of-the-art synthetic-to-real semantic segmentation adaptation on GTA5-to-Cityscapes and SYNT...

  2. Generating All the Roads to Rome: Road Layout Randomization for Improved Road Marking Segmentation

    cs.RO 2019-07 unverdicted novelty 6.0 of 10

    Road layout randomization on semantic labels produces synthetic training pairs that improve mIoU for rare road marking classes by over 12 percentage points in real-world urban deployment while retaining performance on...

  3. Don't Worry About the Weather: Unsupervised Condition-Dependent Domain Adaptation

    cs.CV 2019-07 unverdicted novelty 5.0 of 10

    Lightweight input adapters preprocess images to match ideal-condition training data for off-the-shelf CV models, enabling self-supervised incremental adaptation and reported gains in segmentation and localization on R...

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