Correlated discrete data generation using adversarial training

By Shreyas Patel, Ashutosh Kakadiya, Maitrey Mehta, Raj Derasari, Rahul Patel, Ratnik Gandhi. 2018

20 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Lei Xu
2. Sergey I. Nikolenko
3. Kalyan Veeramachaneni
4. Stefan Lessmann
5. Justin Engelmann

Citations in the corpus

• Synthesizing Tabular Data using Generative Adversarial Networks (2018, background): “RGAN and RCGAN [13] can generate real-valued time-series data. medGAN [9], corrGAN [33] and several improved models [2, 6, 47, 3, 44] can generate discrete medical records but do not tackle the complexity in generating multimodal continuous variables. ehrGAN [8] generates augmented medical records…”
• Synthetic Data for Deep Learning (2019, methodology): “[440] present the CorrGAN model for correlated discrete data”