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Novel deep neural network based pattern field classification architectures.

Publication ,  Journal Article
Huang, K; Zhang, S; Zhang, R; Hussain, A
Published in: Neural networks : the official journal of the International Neural Network Society
July 2020

Field classification is a new extension of traditional classification frameworks that attempts to utilize consistent information from a group of samples (termed fields). By forgoing the independent identically distributed (i.i.d.) assumption, field classification can achieve remarkably improved accuracy compared to traditional classification methods. Most studies of field classification have been conducted on traditional machine learning methods. In this paper, we propose integration with a Bayesian framework, for the first time, in order to extend field classification to deep learning and propose two novel deep neural network architectures: the Field Deep Perceptron (FDP) and the Field Deep Convolutional Neural Network (FDCNN). Specifically, we exploit a deep perceptron structure, typically a 6-layer structure, where the first 3 layers remove (learn) a 'style' from a group of samples to map them into a more discriminative space and the last 3 layers are trained to perform classification. For the FDCNN, we modify the AlexNet framework by adding style transformation layers within the hidden layers. We derive a novel learning scheme from a Bayesian framework and design a novel and efficient learning algorithm with guaranteed convergence for training the deep networks. The whole framework is interpreted with visualization features showing that the field deep neural network can better learn the style of a group of samples. Our developed models are also able to achieve transfer learning and learn transformations for newly introduced fields. We conduct extensive comparative experiments on benchmark data (including face, speech, and handwriting data) to validate our learning approach. Experimental results demonstrate that our proposed deep frameworks achieve significant improvements over other state-of-the-art algorithms, attaining new benchmark performance.

Duke Scholars

Published In

Neural networks : the official journal of the International Neural Network Society

DOI

EISSN

1879-2782

ISSN

0893-6080

Publication Date

July 2020

Volume

127

Start / End Page

82 / 95

Related Subject Headings

  • Pattern Recognition, Automated
  • Neural Networks, Computer
  • Machine Learning
  • Humans
  • Handwriting
  • Deep Learning
  • Biometric Identification
  • Bayes Theorem
  • Artificial Intelligence & Image Processing
  • Algorithms
 

Citation

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Huang, K., Zhang, S., Zhang, R., & Hussain, A. (2020). Novel deep neural network based pattern field classification architectures. Neural Networks : The Official Journal of the International Neural Network Society, 127, 82–95. https://doi.org/10.1016/j.neunet.2020.03.011
Huang, Kaizhu, Shufei Zhang, Rui Zhang, and Amir Hussain. “Novel deep neural network based pattern field classification architectures.Neural Networks : The Official Journal of the International Neural Network Society 127 (July 2020): 82–95. https://doi.org/10.1016/j.neunet.2020.03.011.
Huang K, Zhang S, Zhang R, Hussain A. Novel deep neural network based pattern field classification architectures. Neural networks : the official journal of the International Neural Network Society. 2020 Jul;127:82–95.
Huang, Kaizhu, et al. “Novel deep neural network based pattern field classification architectures.Neural Networks : The Official Journal of the International Neural Network Society, vol. 127, July 2020, pp. 82–95. Epmc, doi:10.1016/j.neunet.2020.03.011.
Huang K, Zhang S, Zhang R, Hussain A. Novel deep neural network based pattern field classification architectures. Neural networks : the official journal of the International Neural Network Society. 2020 Jul;127:82–95.
Journal cover image

Published In

Neural networks : the official journal of the International Neural Network Society

DOI

EISSN

1879-2782

ISSN

0893-6080

Publication Date

July 2020

Volume

127

Start / End Page

82 / 95

Related Subject Headings

  • Pattern Recognition, Automated
  • Neural Networks, Computer
  • Machine Learning
  • Humans
  • Handwriting
  • Deep Learning
  • Biometric Identification
  • Bayes Theorem
  • Artificial Intelligence & Image Processing
  • Algorithms