Work

On Domain Generalization for Human Activity Recognition with Mix-Based Methods

Year: 2025

Type: conference-paper

Abstract: Domain generalization (DG) is a challenging problem that involves adapting a model trained on source domains to an unseen target domain.In human activity recognition (HAR), domain shifts often arise from differences in sensor placement, device specifications, or environmental factors, making generalization difficult.In this work, we investigate the effectiveness of mix-based methods like MixStyle and Exact Feature Distribution Mixing (EFDM) when integrated into state-of-the-art models like ResNet and TS2Vec for DG in HAR tasks, leveraging the DAGHAR benchmark.Our results demonstrate that MixStyle significantly outperforms both EFDM and Empirical Risk Minimization approaches, highlighting its effectiveness in addressing domain shifts.* This project was

Source: European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN)

DOI: 10.14428/esann/2025.es2025-135

Authors: Otávio Napoli, Edson Borin

Institution: Universidade Estadual de Campinas (UNICAMP)

Language: English

Topic: Domain Adaptation and Few-Shot Learning

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funder: Conselho Nacional de Desenvolvimento Científico e Tecnológico

Awards: G1891736384, G6760051732, G7947704863