Out-of-distribution learning is concerned with developing methods where the distribution of the data processed at test time may be different from the distribution of the data used during the training of a model. These techniques go beyond the simple fine-tuning of models. They include techniques such as domain adaptation (Keaton et al., 2023) (Motiian et al., 2017) (Motiian et al., 2017), where models undergo additional training with special losses to adapt to novel training data distributions. OOD techniques aim to go beyong domain generalization (Motiian et al., 2017) where models are made robust to any distortion of the training data distribution, and attempt to produce models that can generalize the original task to something new, sometimes in a compositional fashion (Yao & Doretto, 2010) by leveraging what the model has learned so far. We apply these techniques to several computer vision tasks, including object detection, objects clssification, medical imaging applications, and immersive analytics.

Transfer Learning from Multiple Sources

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Transfer learning allows leveraging the knowledge of source domains, available a priori, to help training a classifier for a target domain, where the availab...


  1. WACV
    CellTranspose: Few-shot Domain Adaptation for Cellular Instance Segmentation Keaton, M. R., Zaveri, R. J., and Doretto, G. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2023. abstract bibTeX arXiv pdf
  2. NIPS
    Few-shot adversarial domain adaptation Motiian, S., Jones, Q., Iranmanesh, S. M., and Doretto, G. In Advances in Neural Information Processing Systems, 2017. abstract bibTeX arXiv pdf
  3. ICCV
    Unified deep supervised domain adaptation andgeneralization Motiian, S., Piccirilli, M., and Adjeroh, G. In Proceedings of IEEE International Conference on Computer Vision, 2017. bibTeX arXiv pdf webpage code
  4. CVPR
    Boosting for transfer learning with multiple sources Yao, Y., and Doretto, G. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2010. abstract bibTeX pdf