Synthetic Data Generation Using Generative Adversarial Networks for Machine Learning Applications

Machine learning algorithms require vast amount of high quality data for learning. However, obtaining such practical sets for use can be a challenge due to privacy regulations, data a pittance, and utter costs for data collection and annotations. These issues pose significant barriers for researchers who rely heavily on vast data sets to engineer machine learning algorithms. This paper presents a solution to this problem by proposing an innovative generative artificial intelligence framework that generate a large scale of synthetic data sets from Generative Adversarial Networks (GANs). The GAN framework learns the distribution of the underlying real data set, and then generate artificial samples that are statistically similar to the real data. The synthetic data generated may be used to diversify the training data set to improve the quality and performance of machine learning algorithms. Based on experimental observation, the study reveal that the artificial data remain statistically similar to the original data set, and can be effectively apply in the field of machine learning and protect the privacy of the original data set.