Paper Info
Title
Examination Of The Deep Neural Networks In Classification Of Distorted Signals
Abstract
Classification of distorted patterns poses real problem for majority of classifiers. In this paper we analyse robustness of deep neural network in classification of such patterns. Using specific convolutional network architecture, an impact of different types of noise on classification accuracy is evaluated. For highly distorted patterns to improve accuracy we propose a preprocessing method of input patterns. Finally, an influence of different types of noise on classification accuracy is also analysed.
Year
DOI
Venue
2016
10.1007/978-3-319-39384-1_60
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, (ICAISC 2016), PT II
Keywords
Field
DocType
Noise, Image recognition, Convolutional neural networks
Pattern recognition,Convolutional neural network,Computer science,Network architecture,Robustness (computer science),Preprocessor,Artificial intelligence,Artificial neural network,Deep neural networks
Conference
Volume
ISSN
Citations 
9693
0302-9743
2
PageRank 
References 
Authors
0.39
6
2
Name
Order
Citations
PageRank
Michal Koziarski1183.66
Boguslaw Cyganek214524.53