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Target to Source Coordinate-wise Adaptation of Pre-trained Models

Luxin Zhang 1, 2 Pascal Germain 3, 2 Yacine Kessaci 1 Christophe Biernacki 2
2 MODAL - MOdel for Data Analysis and Learning
Inria Lille - Nord Europe, LPP - Laboratoire Paul Painlevé - UMR 8524, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille, Université de Lille, Sciences et Technologies
Abstract : Domain adaptation aims to alleviate the gap between source and target data drawn from different distributions. Most of the related works seek either for a latent space where source and target data share the same distribution, or for a transformation of the source distribution to match the target one. In this paper, we introduce an original scenario where the former trained source model is directly reused on target data, requiring only finding a transformation from the target domain to the source domain. As a first approach to tackle this problem, we propose a greedy coordinate-wise transformation leveraging on optimal transport. Beyond being fully independent of the model initially learned on the source data, the achieved transformation has the following three assets: scalability, interpretability and feature-type free (continuous and/or categorical). Our procedure is numerically evaluated on various real datasets, including domain adaptation benchmarks and also a challenging fraud detection dataset with very imbalanced classes. Interestingly, we observe that transforming a small subset of the target features leads to accuracies competitive with "classical" domain adaptation methods.
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Contributor : Christophe Biernacki <>
Submitted on : Wednesday, December 23, 2020 - 4:22:58 PM
Last modification on : Tuesday, December 29, 2020 - 3:29:46 AM


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  • HAL Id : hal-03087284, version 1



Luxin Zhang, Pascal Germain, Yacine Kessaci, Christophe Biernacki. Target to Source Coordinate-wise Adaptation of Pre-trained Models. ECML PKDD 2020 - The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, Sep 2020, Ghent / Virtual, Belgium. ⟨hal-03087284⟩



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