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Home / Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence

Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks

February 1, 2023

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Authors

Henrique Siqueira

University of Hamburg


Sven Magg

University of Hamburg


Stefan Wermter

University of Hamburg


DOI:

10.1609/aaai.v34i04.6037


Abstract:

Ensemble methods, traditionally built with independently trained de-correlated models, have proven to be efficient methods for reducing the remaining residual generalization error, which results in robust and accurate methods for real-world applications. In the context of deep learning, however, training an ensemble of deep networks is costly and generates high redundancy which is inefficient. In this paper, we present experiments on Ensembles with Shared Representations (ESRs) based on convolutional networks to demonstrate, quantitatively and qualitatively, their data processing efficiency and scalability to large-scale datasets of facial expressions. We show that redundancy and computational load can be dramatically reduced by varying the branching level of the ESR without loss of diversity and generalization power, which are both important for ensemble performance. Experiments on large-scale datasets suggest that ESRs reduce the remaining residual generalization error on the AffectNet and FER+ datasets, reach human-level performance, and outperform state-of-the-art methods on facial expression recognition in the wild using emotion and affect concepts.

Topics: AAAI

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HOW TO CITE:

Henrique Siqueira||Sven Magg||Stefan Wermter Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks Proceedings of the AAAI Conference on Artificial Intelligence (2020) 5800-5809.

Henrique Siqueira||Sven Magg||Stefan Wermter Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks AAAI 2020, 5800-5809.

Henrique Siqueira||Sven Magg||Stefan Wermter (2020). Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 5800-5809.

Henrique Siqueira||Sven Magg||Stefan Wermter. Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence 2020 p.5800-5809.

Henrique Siqueira||Sven Magg||Stefan Wermter. 2020. Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks. "Proceedings of the AAAI Conference on Artificial Intelligence". 5800-5809.

Henrique Siqueira||Sven Magg||Stefan Wermter. (2020) "Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks", Proceedings of the AAAI Conference on Artificial Intelligence, p.5800-5809

Henrique Siqueira||Sven Magg||Stefan Wermter, "Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks", AAAI, p.5800-5809, 2020.

Henrique Siqueira||Sven Magg||Stefan Wermter. "Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, 2020, p.5800-5809.

Henrique Siqueira||Sven Magg||Stefan Wermter. "Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks". Proceedings of the AAAI Conference on Artificial Intelligence, (2020): 5800-5809.

Henrique Siqueira||Sven Magg||Stefan Wermter. Efficient Facial Feature Learning with Wide Ensemble-Based Convolutional Neural Networks. AAAI[Internet]. 2020[cited 2023]; 5800-5809.


ISSN: 2374-3468


Published by AAAI Press, Palo Alto, California USA
Copyright 2022, Association for the Advancement of
Artificial Intelligence 1900 Embarcadero Road, Suite
101, Palo Alto, California 94303 All Rights Reserved

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