標題: ZipNet: ZFNet-level Accuracy with 48 x Fewer Parameters
作者: Antioquia, Arren Matthew C.
Tan, Daniel Stanley
Azcarraga, Arnulfo
Cheng, Wen-Huang
Hua, Kai-Lung
電子工程學系及電子研究所
Department of Electronics Engineering and Institute of Electronics
關鍵字: Convolutional Neural Networks;Model Compression;Image Classification;Object Classification;Deep Learning
公開日期: 1-Jan-2018
摘要: 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 48.5x smaller model size and 48.7x 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.
URI: http://hdl.handle.net/11536/153287
ISBN: 978-1-5386-4458-4
期刊: 2018 IEEE INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (IEEE VCIP)
起始頁: 0
結束頁: 0
Appears in Collections:Conferences Paper