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Maxout Networks for Visual Recognition

Maxout Networks for Visual Recognition

Gabriel Castaneda, Paul Morris, Taghi M. Khoshgoftaar
Copyright: © 2019 |Volume: 10 |Issue: 4 |Pages: 25
ISSN: 1947-8534|EISSN: 1947-8542|EISBN13: 9781522565352|DOI: 10.4018/IJMDEM.2019100101
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MLA

Castaneda, Gabriel, et al. "Maxout Networks for Visual Recognition." IJMDEM vol.10, no.4 2019: pp.1-25. http://doi.org/10.4018/IJMDEM.2019100101

APA

Castaneda, G., Morris, P., & Khoshgoftaar, T. M. (2019). Maxout Networks for Visual Recognition. International Journal of Multimedia Data Engineering and Management (IJMDEM), 10(4), 1-25. http://doi.org/10.4018/IJMDEM.2019100101

Chicago

Castaneda, Gabriel, Paul Morris, and Taghi M. Khoshgoftaar. "Maxout Networks for Visual Recognition," International Journal of Multimedia Data Engineering and Management (IJMDEM) 10, no.4: 1-25. http://doi.org/10.4018/IJMDEM.2019100101

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Abstract

This study investigates the effectiveness of multiple maxout activation variants on image classification, facial identification and verification tasks using convolutional neural networks. A network with maxout activation has a higher number of trainable parameters compared to networks with traditional activation functions. However, it is not clear if the activation function itself or the increase in the number of trainable parameters is responsible for yielding the best performance on different entity recognition tasks. This article investigates if an increase in the number of convolutional filters on the rectified linear unit activation performs equal-to or better-than maxout networks. Our experiments compare rectified linear unit, leaky rectified linear unit, scaled exponential linear unit, and hyperbolic tangent to four maxout variants. Throughout the experiments, we found that on average, across all datasets, the rectified linear unit networks perform better than any maxout activation when the number of convolutional filters is increased six times.

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