Abstract
| Original language | English |
|---|---|
| Title of host publication | ICBDE 2019 |
| DOIs | |
| Publication status | Published - 31 Mar 2019 |
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Keywords
- feature cnn
- machine learning
- scaling
- mnist
- resnet
- deep learning
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Application of a Scaled MNIST Dataset Blended with Natural Scene Background on ResNet. / Marinov, Alexander; Mtetwa, Nhamo; Larijani, Hadi.
ICBDE 2019. 2019.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Application of a Scaled MNIST Dataset Blended with Natural Scene Background on ResNet
AU - Marinov, Alexander
AU - Mtetwa, Nhamo
AU - Larijani, Hadi
N1 - Acceptance in SAN DOI not found Accepted papers will be published in the International Conference Proceedings Series by ACM, which will be archived in the ACM Digital Library, and sent to be indexed by EI Compendex and Scopus and submitted to be reviewed by Thomson Reuters Conference Proceedings Citation Index (ISI Web of Science). AAM: made open in line with publisher policy (default ACM via Romeo) ET 5/7/19
PY - 2019/3/31
Y1 - 2019/3/31
N2 - Deep learning (DL) has gained a lot of popularity in the science and business community. It has been successful in a range of applications, especially in computer vision. This paper presents results from applying scaled MNIST images dataset to a popular implementation of deep learning called ResNet. This is a valuable contribution because in general convolutional networks are not scale invariant. Our objective is to explore the behavior of a residual neural network when trained and evaluated using three different datasets of scaled MNIST images.
AB - Deep learning (DL) has gained a lot of popularity in the science and business community. It has been successful in a range of applications, especially in computer vision. This paper presents results from applying scaled MNIST images dataset to a popular implementation of deep learning called ResNet. This is a valuable contribution because in general convolutional networks are not scale invariant. Our objective is to explore the behavior of a residual neural network when trained and evaluated using three different datasets of scaled MNIST images.
KW - feature cnn
KW - machine learning
KW - scaling
KW - mnist
KW - resnet
KW - deep learning
U2 - 10.1145/3322134.3322147
DO - 10.1145/3322134.3322147
M3 - Conference contribution
SN - 9781450361866
BT - ICBDE 2019
ER -