Data Augmentation Using GANs
This addresses data scarcity and privacy issues in machine learning, but it is incremental as it applies an existing method (GANs) to data augmentation.
The paper tackles the problem of limited or sensitive training data by using Generative Adversarial Networks (GANs) to generate artificial data, showing that a Decision Tree classifier trained on GAN-generated data achieved similar or sometimes better accuracy and recall compared to training on original data.
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.