Abstract
This paper proposes a concept of Digital Stereotypes, observed during research on quantitative overrepresentation of one class over others, and its impact on the results of the training of Deep Learning models. The real-life observed data classes are rarely of the same size, and the intuition of presenting multiple examples of one class and then showing a few counterexamples may be very misleading in multimodal classification. Deep Learning models, when taught with overrepresentation, may produce incorrect inferring results, similar to stereotypes. The generic idea of stereotypes seems to be helpful for categorisation from the training point of view, but it has a negative influence on the inferring result. Authors evaluate a large dataset in various scenarios: overrepresentation of one or two classes, underrepresentation of some classes, and same-size (trimmed) classes. The presented research can be applied to any multiclassification applications, but it may be especially important in AI, where the classification, uncertainty and building new knowledge overlap. This paper presents specific ’decreases in accuracy’ observed within multiclassification of unleveled datasets. The ’decreases in accuracy’, named by the authors ’stereotypes’, can also bring an inspiring insight into other fields and applications, not only multimodal sentiment analysis.
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference45 articles.
1. The Pepper humanoid robot in front desk application;Gardecki;Proceedings of the 2018 Progress in Applied Electrical Engineering,2018
2. Humanoid Receptionist Connected to IoT Subsystems and Smart Infrastructure is Smarter than Expected
3. The Need for Robots in Global Health
4. Mobilność, interaktywność i zaangażowanie użytkowników jako wyzwania współczesnej komunikacji w biznesie;Badzińska;Marketing i Zarządzanie,2015
5. Self‐service with a smile?
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