DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation

Bharath Bhushan Damodaran, Benjamin Kellenberger, Remi Flamary, Devis Tuia, Nicolas Courty

ECCV 2018

doi:10.1007/978-3-030-01225-0_28 /eccv/2018/bhushandamodaran2018eccv-deepjdot/

Abstract

In computer vision, one is often confronted with problems of domain shifts, which occur when one applies a classifier trained on a source dataset to target data sharing similar characteristics (e.g. same classes), but also different latent data structures (e.g. different acquisition conditions). In such a situation, the model will perform poorly on the new data, since the classifier is specialized to recognize visual cues specific to the source domain. In this work we explore a solution, named DeepJDOT, to tackle this problem: through a measure of discrepancy on joint deep representations/labels based on optimal transport, we not only learn new data representations aligned between the source and target domain, but also simultaneously preserve the discriminative information used by the classifier. We applied DeepJDOT to a series of visual recognition tasks, where it compares favorably against state-of-the-art deep domain adaptation methods.

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Cite

Text

Bhushan Damodaran et al. "DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation." Proceedings of the European Conference on Computer Vision (ECCV), 2018. doi:10.1007/978-3-030-01225-0_28

Markdown

[Bhushan Damodaran et al. "DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation." Proceedings of the European Conference on Computer Vision (ECCV), 2018.](https://mlanthology.org/eccv/2018/bhushandamodaran2018eccv-deepjdot/) doi:10.1007/978-3-030-01225-0_28

BibTeX

@inproceedings{bhushandamodaran2018eccv-deepjdot,
  title     = {{DeepJDOT: Deep Joint Distribution Optimal Transport for Unsupervised Domain Adaptation}},
  author    = {Bhushan Damodaran, Bharath and Kellenberger, Benjamin and Flamary, Remi and Tuia, Devis and Courty, Nicolas},
  booktitle = {Proceedings of the European Conference on Computer Vision (ECCV)},
  year      = {2018},
  doi       = {10.1007/978-3-030-01225-0_28},
  url       = {https://mlanthology.org/eccv/2018/bhushandamodaran2018eccv-deepjdot/}
}