Cubism: Co-balanced Mixup for Unsupervised Volcano-Seismic Knowledge Transfer

Mahsa Keramati, Mohammad A. Tayebi, Zahra Zohrevand, Uwe Glässer, Juan Anzieta, Glyn Williams-Jones

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

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Volcanic eruptions are severe global threats. Forecasting these unrests via monitoring precursory earthquakes is vital for managing the consequent economic and social risks. Due to various contextual factors, volcano-seismic patterns are not spatiotemporal invariant. Training a robust model for any novel volcano-seismic situation relies on a costly, time-consuming and subjective process of manually labeling data; using a model trained on data from another volcano-seismic setting is typically not a viable option. Unsupervised domain adaptation (UDA) techniques address this issue by transferring knowledge extracted from a labeled domain to an unlabeled one. A challenging problem is the inherent imbalance in volcano-seismic data that degrades the efficiency of an adopted UDA technique. Here, we propose a co-balanced UDA approach, called Cubism, to bypass the manual annotation process for any newly monitored volcano by utilizing the patterns recognized in a different volcano-seismic dataset with labels. Employing an invertible latent space, Cubism alternates between a co-balanced generation of semantically meaningful inter-volcano samples and UDA. Inter-volcano samples are generated via the mixup data augmentation technique. Due to the sensitivity of mixup to data imbalance, Cubism introduces a novel co-balanced ratio that regulates the generation of inter-volcano samples considering the conditional distributions of both volcanoes. To the best of our knowledge, Cubism is the first UDA-based approach that transfers volcano-seismic knowledge without any supervision of an unseen volcano-seismic situation. Our extensive experiments show that Cubism significantly outperforms baseline methods and effectively provides a robust cross-volcano classifier.

Idioma originalInglés
Título de la publicación alojadaMachine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2022, Proceedings
EditoresMassih-Reza Amini, Stéphane Canu, Asja Fischer, Tias Guns, Petra Kralj Novak, Grigorios Tsoumakas
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas17
ISBN (versión impresa)9783031264184
EstadoPublicada - 2023
Publicado de forma externa
Evento22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022 - Grenoble, Francia
Duración: 19 sep. 202223 sep. 2022

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen13717 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349


Conferencia22nd Joint European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, ECML PKDD 2022

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Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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