Data Augmentation Using GANs

2019/04/19 by Fabio Henrique Kiyoiti dos Santos Tanaka, Tanaka, Fabio Henrique Kiyoiti dos Santos, Claus Aranha +1 · 5 citations
Computer Science · #Anomaly Detection Techniques and Applications #Digital Media Forensic Detection #FOS: Computer and information sciences #Generative Adversarial Networks and Image Synthesis #Machine Learning (cs.LG) #Machine Learning (stat.ML)

paper · pdf · doi:10.48550/arxiv.1904.09135

openalex publication_date 2019/04/19 · openalex created_date 2019/04/25 · openalex updated_date 2026/07/28

Abstract

In this paper we propose the use of Generative Adversarial Networks (GAN) to generate artificial training data for machine learning tasks. The generation of artificial training data can be extremely useful in situations such as imbalanced data sets, performing a role similar to SMOTE or ADASYN. It is also useful when the data contains sensitive information, and it is desirable to avoid using the original data set as much as possible (example: medical data). We test our proposal on benchmark data sets using different network architectures, and show that a Decision Tree (DT) classifier trained using the training data generated by the GAN reached the same, (and surprisingly sometimes better), accuracy and recall than a DT trained on the original data set.

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