  Generative Adversarial Networks (GAN) have shown great promise in tasks like
synthetic image generation, image inpainting, style transfer, and anomaly
detection. However, generating discrete data is a challenge. This work presents
an adversarial training based correlated discrete data (CDD) generation model.
It also details an approach for conditional CDD generation. The results of our
approach are presented over two datasets; job-seeking candidates skill set
(private dataset) and MNIST (public dataset). From quantitative and qualitative
analysis of these results, we show that our model performs better as it
leverages inherent correlation in the data, than an existing model that
overlooks correlation.
