Autonomous Choice of Deep Neural Network Parameters by a Modified Generative Adversarial Network

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

The choice of parameters, and the design of the network architecture are important factors affecting the performance of deep neural networks. However, this task still heavily depends on trial and error, and empirical results. Considering that there are many design and parameter choices, it is very hard to cover every configuration, and find the optimal structure. In this paper, we propose a novel method that autonomously and simultaneously optimizes multiple parameters of any given deep neural network by using a modified generative adversarial network (GAN). In our approach, two different models compete and improve each other progressively. Without loss of generality, the proposed method has been tested with three different neural network architectures, and three very different datasets and applications. The results show that the presented approach can simultaneously and successfully optimize multiple neural network parameters, and achieve increased accuracy in all three scenarios.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Image Processing, ICIP 2019 - Proceedings
PublisherIEEE Computer Society
Pages3846-3850
Number of pages5
ISBN (Electronic)9781538662496
DOIs
StatePublished - Sep 2019
Event26th IEEE International Conference on Image Processing, ICIP 2019 - Taipei, Taiwan, Province of China
Duration: Sep 22 2019Sep 25 2019

Publication series

NameProceedings - International Conference on Image Processing, ICIP
Volume2019-September
ISSN (Print)1522-4880

Conference

Conference26th IEEE International Conference on Image Processing, ICIP 2019
CountryTaiwan, Province of China
CityTaipei
Period9/22/199/25/19

Keywords

  • Deep learning
  • generative adversarial networks
  • neural networks
  • parameter choice

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

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