Article

Confounder-Invariant Representation Learning (CIRL) for Robust Olfaction with Scarce Aroma Sensor Data

2025-09-12

Abstract excerpt

Confounding factors in olfactory aroma data, such as high humidity levels, substantially affect sensor outputs, masking subtle volatile organic compound (VOC) patterns and hindering generalizable machine learning models. Traditional representation learning methods often require large datasets to mitigate confounder-induced variance, a resource unavailable in specialized sensor applications with limited data. This...

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Literature Corpus work: 5e8e663e-4f41-5638-9316-ec12e5916f91
DOI: 10.20944/preprints202509.0988.v1

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