Abstract
In domain generalization the target domain is not known at training time. We show that a style transfer based data augmentation strategy can be implemented easily and outperforms the current state of the art domain generalization methods. Moreover, we observe that those methods, even if combined with the described data augmentation, do not take advantage of it, indicating the need of new generalization solutions.
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Computational resources provided by hpc@polito: (http://hpc.polito.it).
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Borlino, F.C., D’Innocente, A., Tommasi, T. (2020). Domain Generalization vs Data Augmentation: An Unbiased Perspective. In: Bartoli, A., Fusiello, A. (eds) Computer Vision – ECCV 2020 Workshops. ECCV 2020. Lecture Notes in Computer Science(), vol 12535. Springer, Cham. https://doi.org/10.1007/978-3-030-66415-2_50
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DOI: https://doi.org/10.1007/978-3-030-66415-2_50
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