Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation

@article{Barbato2021LatentSR,
  title={Latent Space Regularization for Unsupervised Domain Adaptation in Semantic Segmentation},
  author={Francesco Barbato and Marco Toldo and Umberto Michieli and Pietro Zanuttigh},
  journal={2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)},
  year={2021},
  pages={2829-2839}
}
  • F. Barbato, Marco Toldo, +1 author P. Zanuttigh
  • Published 6 April 2021
  • Computer Science
  • 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Deep convolutional neural networks for semantic segmentation achieve outstanding accuracy, however they also have a couple of major drawbacks: first, they do not generalize well to distributions slightly different from the one of the training data; second, they require a huge amount of labeled data for their optimization. In this paper, we introduce feature-level space-shaping regularization strategies to reduce the domain discrepancy in semantic segmentation. In particular, for this purpose we… Expand

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