ZipNet: ZFNet-level Accuracy with 48× Fewer Parameters

Arren Matthew C. Antioquia, Daniel Stanley Tan, Arnulfo Azcarraga, Wen-Huang Cheng, Kai Lung Hua

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

Abstract

With the introduction of Convolutional Neural Networks, models for image classification achieve higher classification accuracy. Based on the pattern of the design of CNN architectures, increasing the number of layers equates to a higher classification accuracy, but also increases the number of parameters and model size. This negatively affects the model training time, processing time, and memory requirement. We develop ZipNet, a CNN architecture with a higher classification accuracy than ZFNet, the winner of ILSVRC 2013, but with 48.5× smaller model size and 48.7× fewer parameters. The classification accuracy of ZipNet is higher than the performance of ZFNet and SqueezeNet on all configurations of the Caltech-256 dataset with varying number of training examples.

Original languageEnglish
Title of host publicationVCIP 2018 - IEEE International Conference on Visual Communications and Image Processing
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538644584
DOIs
StatePublished - 2 Jul 2018
Event33rd IEEE International Conference on Visual Communications and Image Processing, VCIP 2018 - Taichung, Taiwan
Duration: 9 Dec 201812 Dec 2018

Publication series

NameVCIP 2018 - IEEE International Conference on Visual Communications and Image Processing

Conference

Conference33rd IEEE International Conference on Visual Communications and Image Processing, VCIP 2018
CountryTaiwan
CityTaichung
Period9/12/1812/12/18

Keywords

  • Convolutional Neural Networks
  • Deep Learning
  • Image Classification
  • Model Compression
  • Object Classification

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  • Cite this

    Antioquia, A. M. C., Tan, D. S., Azcarraga, A., Cheng, W-H., & Hua, K. L. (2018). ZipNet: ZFNet-level Accuracy with 48× Fewer Parameters. In VCIP 2018 - IEEE International Conference on Visual Communications and Image Processing [8698672] (VCIP 2018 - IEEE International Conference on Visual Communications and Image Processing). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/VCIP.2018.8698672