Robust Convolutional Neural Networks for Image Recognition

Autor:	Naz E. Islam, Hayder M. Albeahdili, Haider A. Alwzwazy
Rok vydání:	2015
Předmět:	General Computer Science Time delay neural network Computer science business.industry Computer vision Artificial intelligence Machine learning computer.software_genre business Convolutional neural network computer MNIST database Task (project management)
Zdroj:	International Journal of Advanced Computer Science and Applications. 6
ISSN:	2156-5570 2158-107X
DOI:	10.14569/ijacsa.2015.061115
Popis:	Recently image recognition becomes vital task using several methods. One of the most interesting used methods is using Convolutional Neural Network (CNN). It is widely used for this purpose. However, since there are some tasks that have small features that are considered an essential part of a task, then classification using CNN is not efficient because most of those features diminish before reaching the final stage of classification. In this work, analyzing and exploring essential parameters that can influence model performance. Furthermore different elegant prior contemporary models are recruited to introduce new leveraging model. Finally, a new CNN architecture is proposed which achieves state-of-the-art classification results on the different challenge benchmarks. The experimented are conducted on MNIST, CIFAR-10, and CIFAR-100 datasets. Experimental results showed that the results outperform and achieve superior results comparing to the most contemporary approaches.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::8aee8ad8966152cbb302c2755590e857 https://doi.org/10.14569/ijacsa.2015.061115 Zobrazit plný text záznamu