Open AccessComparative Study of Various Convolutional Neural Networks on Cifar-10
Author(s) -
Tushar Goyal
Publication year - 2020
Publication title -
international journal of modern trends in science and technology
Language(s) - English
Resource type - Journals
ISSN - 2455-3778
DOI - 10.46501/ijmtst061276
Subject(s) - convolutional neural network , computer science , artificial intelligence , field (mathematics) , scratch , pattern recognition (psychology) , neocognitron , artificial neural network , state (computer science) , deep learning , image (mathematics) , cellular neural network , machine learning , time delay neural network , mathematics , algorithm , pure mathematics , operating system
Image recognition plays a foundational role in the field of computer vision and there has been extensiveresearch to develop state-of-the-art techniques especially using Convolutional Neural Network (CNN). Thispaper aims to study some CNNs, heavily inspired by highly popular state-of-the-art CNNs, designed fromscratch specifically for the Cifar-10 dataset and present a fair comparison between them.