ZipNet: ZFNet-level accuracy with 48× fewer parameters

ZipNet: ZFNet-level accuracy with 48× fewer parameters

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 param...

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Bibliographic Details
Main Authors: Antioquia, Arren Matthew C., Tan, Daniel Stanley, Azcarraga, Arnulfo P., Cheng, Wen Huang, Hua, Kai Lung
Format: text
Published: Animo Repository 2018
Subjects:
Neural networks (Computer science)
Image converters
Visual communication
Computer Sciences
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/3024
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Institution: De La Salle University
Description
Summary: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. © 2018 IEEE.

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