Drift detection on feature attributions for monitoring visual reinforcement learning models in maritime port surveillance

FADMON can add an explainability layer to the monitoring system while also supporting detection of changes in the underlying interpretation of the input data by the model, monitoring the concept rather than the data, while matching established drift detection methods metrics-wise.

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Publication Details

Journal
Open Research Europe
Published
2026-01-02
DOI
https://doi.org/10.12688/openreseurope.22116.1
Primary Topic
Data Stream Mining Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Drift detection on feature attributions for monitoring visual reinforcement learning models in maritime port surveillance

Luis Unzueta, Francisco Javier Iriarte, Ignacio Arganda-Carreras, Beatrice Azoubel et al.
Open Research Europe
Data Stream Mining Techniques
article

Drift detection on feature attributions for monitoring visual reinforcement learning models in maritime port surveillance

Luis Unzueta, Francisco Javier Iriarte, Ignacio Arganda-Carreras, Beatrice Azoubel, Andrés Chica Linares, Adrián Carrizo-Pérez
article en

Abstract

FADMON can add an explainability layer to the monitoring system while also supporting detection of changes in the underlying interpretation of the input data by the model, monitoring the concept rather than the data, while matching established drift detection methods metrics-wise.

Open Research EuropeVol. 6
Ikerbasque (ES), University of the Basque Country (ES), Indra (Spain) (ES), Vicomtech (ES), Fundación Biofísica Bizkaia (ES), Donostia International Physics Center (ES)
European Commission
Openalex Percentile: Top 98%
Data Stream Mining Techniques
DOIOpen Access PDFOpenAlex
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