Target to Source Coordinate-wise Adaptation of Pre-trained Models - INRIA - Institut National de Recherche en Informatique et en Automatique Accéder directement au contenu
Communication Dans Un Congrès Année : 2020

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

Yacine Kessaci
  • Fonction : Auteur
  • PersonId : 1086800
Christophe Biernacki
  • Fonction : Auteur
  • PersonId : 923939

Résumé

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.
Fichier principal
Vignette du fichier
sub_957.pdf (483.67 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03087284 , version 1 (23-12-2020)

Identifiants

  • HAL Id : hal-03087284 , version 1

Citer

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⟩
129 Consultations
163 Téléchargements

Partager

Gmail Facebook X LinkedIn More