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domain shift

时间: 2023-11-25 21:33:24 浏览: 66

Optimal Transport for Domain Adaptation

Domain adaptation is one of the most challenging tasks of modern data analytics. If the adaptation is done correctly, models built on a specific data representation become more robust when confronted to data depicting the same classes, but described by another observation system. Among the many strategies proposed, finding domain-invariant representations has shown excellent properties, in particular since it allows to train a unique classifier effective in all domains. In this paper, we propose a regularized unsupervised optimal transportation model to perform the alignment of the representations in the source and target domains. We learn a transportation plan matching both PDFs, which constrains labeled samples of the same class in the source domain to remain close during transport. This way, we exploit at the same time the labeled samples in the source and the distributions observed in both domains. Experiments on toy and challenging real visual adaptation examples show the interest of the method, that consistently outperforms state of the art approaches. In addition, numerical experiments show that our approach leads to better performances on domain invariant deep learning features and can be easily adapted to the semi-supervised case where few labeled samples are available in the target domain.

Domain shift refers to the phenomenon where the distribution of data in the target domain is different from that in the source domain. This can lead to a decrease in performance of machine learning models when applied to the target domain, as the model may not have been trained on enough examples from the target domain to generalize well. Domain shift can occur for a variety of reasons, such as changes in the data collection process, changes in the underlying problem being solved, or changes in the environment in which the data is being collected. To mitigate the effects of domain shift, techniques such as domain adaptation and transfer learning can be used to help the model adapt to the new domain.

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