Dead pixel test using effective receptive field

•Empirical observations on convolutional neural networks are presented.•The size of the receptive field did not guarantee improved accuracy.•We observed that dead pixels exist in modern convolutional neural networks.•The effect of dead pixels is discussed with a solution to resolve them. Deep neural...

Full description

Saved in:
Bibliographic Details
Published in:Pattern recognition letters 2023-03, Vol.167, p.149-156
Main Authors: Kim, Bum Jun, Choi, Hyeyeon, Jang, Hyeonah, Lee, Dong Gu, Jeong, Wonseok, Kim, Sang Woo
Format: Article
Language:English
Subjects:
Computer vision
Convolutional kernel
Convolutional neural networks
Deep learning
Model interpretation
Receptive field
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Empirical observations on convolutional neural networks are presented.•The size of the receptive field did not guarantee improved accuracy.•We observed that dead pixels exist in modern convolutional neural networks.•The effect of dead pixels is discussed with a solution to resolve them. Deep neural networks have been used in various fields, but their internal behavior in how they understand images is not well known. In this study, we discuss two counterintuitive properties of convolutional neural networks (CNNs). First, we evaluated the size of the receptive field of CNNs with their classification accuracy. Previous studies have attempted to increase the size of the receptive field for performance gain. However, we observed that some CNNs with a smaller receptive field can achieve higher classification accuracy. In this regard, we claim that a larger receptive field does not guarantee improved classification accuracy. Second, using the effective receptive field, we examined the contribution of each pixel to the output of CNN. Intuitively, each pixel is expected to equally contribute to the final output, but we found that there exist pixels in a partially dead state with little contribution to the output. We reveal that the reason for dead pixels lies in even stride operations with odd-sized kernels in CNN and propose a kernel padding method to remove the dead pixels. We demonstrated the vulnerability of CNNs with dead pixels when we detect a noise or small box that is on dead pixels. Our findings on dead pixels should be understood and considered in practical applications of CNN.
ISSN:0167-8655
1872-7344
DOI:10.1016/j.patrec.2023.02.018