.png){kind=icon}
.png){kind=icon}
Return
Robust convolution kernel quantity determination based on corner radiation area adaptation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.neucom.2018.07.054.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The number of kernels in a convolutional neural network (CNN) can have a significant impact on the performance of the CNN in the aspects of accuracy and computation efficiency. However, existing approaches to determining the number of convolution kernels are mainly conducted through a manual process, which suffers from the problems of potential overfitting, instability and inefficiency. In response to these problems, this paper presents a corner radiation area adaptation (CRAA) based method to automatically determine the number of convolution kernels. CRAA is evaluated in comparison with three representative methods on multiple public data sets. Experimental results show that CRAA is robust to the number of convolution kernels which is well adapted to a specific data set and achieves a higher level of classification accuracy by 3% when spending the same period of time in classification. More importantly, CRAA reduces the computational time by 15% in comparison with the three representative approaches when reaching the same level of accuracy in classification. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Convolutional kernel
Convolutional neural network
Feature extraction
Corner radiation area adaptation
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6.5
Papers:
2.5W
Citations:
6.5W