CLASSIFICATION BASED ON MISSING FEATURES IN DEEP CONVOLUTIONAL NEURAL NETWORKS

Artificial Neural Networks, notably Convolutional Neural Networks (CNN) are widely used for classification purposes in different fields such as image classification, text classification and others. It is not uncommon therefore that these models are used in critical systems (e.g. self-driving cars),...

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Bibliographic Details
Published inNeural network world Vol. 29; no. 4; pp. 221 - 234
Main Authors Milošević, Nemanja, Racković, Miloš
Format Journal Article
LanguageEnglish
Published Prague Czech Technical University in Prague, Faculty of Transportation Sciences 01.01.2019
Subjects
Artificial neural networks
Autonomous cars
Classification
Feature extraction
Image classification
Neural networks
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Summary:Artificial Neural Networks, notably Convolutional Neural Networks (CNN) are widely used for classification purposes in different fields such as image classification, text classification and others. It is not uncommon therefore that these models are used in critical systems (e.g. self-driving cars), where robustness is a very important attribute. All Convolutional Neural Networks used for classification, classify based on the extracted features found in the input sample. In this paper, we present a novel approach of doing the opposite - classification based on features not present in the input sample. Obtained results show not only that this way of learning is indeed possible but also that the trained models become more robust in certain scenarios. The presented approach can be applied to any existing Convolutional Neural Network model and does not require any additional training data.
ISSN:1210-0552
2336-4335
DOI:10.14311/NNW.2019.29.0015