Software Engineering for Dataset Augmentation using Generative Adversarial Networks
@article{Jahic2019SoftwareEF,
title={Software Engineering for Dataset Augmentation using Generative Adversarial Networks},
author={Benjamin Jahic and Nicolas Guelfi and B. Ries},
journal={2019 IEEE 10th International Conference on Software Engineering and Service Science (ICSESS)},
year={2019},
pages={59-66}
}Software engineers require a large amount of data for building neural network-based software systems. The engineering of these data is often neglected, though, it is a critical and time-consuming activity. In this work, we present a novel software engineering approach for dataset augmentation using neural networks. We propose a rigorous process for generating synthetic data to improve the training of neural networks. Also, we demonstrate our approach to successfully improve the recognition of… CONTINUE READING
Figures, Tables, and Topics from this paper
2 Citations
References
SHOWING 1-10 OF 24 REFERENCES
Understanding Data Augmentation for Classification: When to Warp?
- Computer Science
- 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
- 2016
- 422
- PDF
Data augmentation for improving deep learning in image classification problem
- Computer Science
- 2018 International Interdisciplinary PhD Workshop (IIPhDW)
- 2018
- 226
Best practices for convolutional neural networks applied to visual document analysis
- Computer Science
- Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings.
- 2003
- 2,050
- PDF
Dropout: a simple way to prevent neural networks from overfitting
- Computer Science
- J. Mach. Learn. Res.
- 2014
- 21,339
- PDF
ImageNet classification with deep convolutional neural networks
- Computer Science
- Commun. ACM
- 2012
- 59,684
- PDF
SMOTE: Synthetic Minority Over-sampling Technique
- Computer Science, Mathematics
- J. Artif. Intell. Res.
- 2002
- 10,309
- PDF