Work

Online Multi-Source Domain Adaptation Through Gaussian Mixtures and Dataset Dictionary Learning

Year: 2024

Type: conference-paper

Abstract: This paper addresses the challenge of online multi-source domain adaptation (MSDA) in transfer learning, a scenario where one needs to adapt multiple, heterogeneous source domains towards a target domain that comes in a stream. We introduce a novel approach for the online fit o f a Gaussian Mixture Model (GMM), based on the Wasserstein geometry of Gaussian measures. We build upon this method and recent developments in dataset dictionary learning for proposing a novel strategy in online MSDA. Experiments on the chal-lenging Tennessee Eastman Process benchmark demonstrate that our approach is able to adapt on the fly to the stream of target domain data. Furthermore, our online GMM serves as a memory, representing the whole stream of data.

Source: IEEE International Workshop on Machine Learning for Signal Processing (MLSP)

DOI: 10.1109/mlsp58920.2024.10734818

Authors: Eduardo Fernandes Montesuma, Stevan Le Stanc, Fred Ngolè Mboula

Institutions: Commissariat à l'Énergie Atomique et aux Énergies Alternatives, CEA Paris-Saclay - Etablissement de Saclay

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

OpenAlex ID: W4404037654

FWCI:

Cites: 20

Cited by:

Related to: 10