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2021 | OriginalPaper | Buchkapitel

What Did Our Model Just Learn? Hard Lessons in Applying Deep Learning to Human Factors Data

verfasst von : Brian Weigel, Kaleb Loar, Andrés Colón, Robert Wright

Erschienen in: Advances in Neuroergonomics and Cognitive Engineering

Verlag: Springer International Publishing

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Abstract

Deep learning is revolutionizing all areas of data science, including human factors research. Much of human factors data, however, have fundamental idiosyncrasies that make applying deep learning challenging. Further, the complexity of deep learning can make finding errors challenging and deducing what was actually learned by the model near impossible. This paper provides two case-studies in which our research group faced and overcame such challenges. It examines the root causes of each issue and discusses how they may lead to common challenges. We describe how we discovered problems and describe how we rectified them. It is our hope, that by sharing our experiences with likely common challenges, we can help other researchers in avoiding similar pitfalls.

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Nächstes Kapitel Addressing Two Central Issues of Team Interaction Dynamics: The Whole is Greater Than the Sum of Its Parts

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Titel: What Did Our Model Just Learn? Hard Lessons in Applying Deep Learning to Human Factors Data
verfasst von: Brian Weigel
Kaleb Loar
Andrés Colón
Robert Wright
Verlag: Springer International Publishing
Buch: Advances in Neuroergonomics and Cognitive Engineering
Print ISBN: 978-3-030-80284-4

Electronic ISBN: 978-3-030-80285-1

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-80285-1_7

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