• Journal of Internet Computing and Services
    ISSN 2287 - 1136(Online) / ISSN 1598 - 0170 (Print)
    http://jics.or.kr/

A layered-wise data augmenting algorithm for small sampling data


Hee-chan Cho, Jong-sub Moon, Journal of Internet Computing and Services, Vol. 20, No. 6, pp. 65-72, Dec. 2019
10.7472/jksii.2019.20.6.65, Full Text:
Keywords: Deep Learning, data augmentation, Eigen decomposition

Abstract

Data augmentation is a method that increases the amount of data through various algorithms based on a small amount of sample data. When machine learning and deep learning techniques are used to solve real-world problems, there is often a lack of data sets. The lack of data is at greater risk of underfitting and overfitting, in addition to the poor reflection of the characteristics of the set of data when learning a model. Thus, in this paper, through the layer-wise data augmenting method at each layer of deep neural network, the proposed method produces augmented data that is substantially meaningful and shows that the method presented by the paper through experimentation is effective in the learning of the model by measuring whether the method presented by the paper improves classification accuracy.


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Cite this article
[APA Style]
Hee-chan Cho and Jong-sub Moon (2019). A layered-wise data augmenting algorithm for small sampling data. Journal of Internet Computing and Services, 20(6), 65-72. DOI: 10.7472/jksii.2019.20.6.65.

[IEEE Style]
H. Cho and J. Moon, "A layered-wise data augmenting algorithm for small sampling data," Journal of Internet Computing and Services, vol. 20, no. 6, pp. 65-72, 2019. DOI: 10.7472/jksii.2019.20.6.65.

[ACM Style]
Hee-chan Cho and Jong-sub Moon. 2019. A layered-wise data augmenting algorithm for small sampling data. Journal of Internet Computing and Services, 20, 6, (2019), 65-72. DOI: 10.7472/jksii.2019.20.6.65.