Generating Synthetic Data for Training Classifiers

The absence of labeled datasets is one of the most significant challenges in machine learning. It requires both time and assets. Generative Adversarial Networks (GANs) have been tremendously successful at generating data. Although GANs can generate data, yet in standard setups, they are unable to produce the corresponding labels and the resulting data often lacks diversity. In this research, I will use conditional GANs to generate a synthetic data set based on Fashion-MNIST and compare the classification performance of models trained on each dataset.Â