Title :

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

Author(s) :

Zhang, Luxin [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Worldline France
Germain, Pascal [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Université Laval [Québec] [ULaval]
Kessaci, Yacine [Auteur]
Worldline France
Biernacki, Christophe [Auteur]
MOdel for Data Analysis and Learning [MODAL]

Conference title :

ECML PKDD 2020 - The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases

City :

Ghent / Virtual

Country :

Belgique

Start date of the conference :

2020-09-14

English keyword(s) :

Domain Adaptation
Optimal Transport
Feature Selection

HAL domain(s) :

Statistiques [stat]/Méthodologie [stat.ME]

English abstract : [en]

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, ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL