Open AccessDeep Residual Learning for Image Classification using Cross Validation
Author(s) -
Kshitij Tripathi,
Anil Kumar Gupta,
Rajendra G. Vyas
Publication year - 2020
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.f4131.049620
Subject(s) - convolutional neural network , residual , computer science , artificial intelligence , residual neural network , contextual image classification , pattern recognition (psychology) , block (permutation group theory) , deep learning , image (mathematics) , popularity , machine learning , mathematics , algorithm , geometry , psychology , social psychology
Convolutional Neural Networks (CNN) are very common now especially in the image classification tasks as CNN’s have better classification accuracy than other techniques available in image classification. Another type of CNN called as Residual Neural Networks (RESNET) are gaining popularity because of better accuracy than normal CNN because of residual block available in it. In the present article the RESNET architecture is used for image classification on CIFAR-10 dataset using cross-validation approach that reflects a consistently better accuracy on the above dataset.