Generative Adversarial Nets: Can we generate a new dataset based on only one training set?

TL;DR

This paper explores extending GANs to generate new datasets with different distributions from the training data, controlling divergence, motivated by applications like creating new rice varieties.

Contribution

It introduces a method for generating datasets with controlled distribution divergence from the original training set using GANs.

Findings

Successfully generated datasets with specified divergence levels.

Demonstrated application in generating new rice types.

Controlled divergence enhances dataset diversity.

Abstract

A generative adversarial network (GAN) is a class of machine learning frameworks designed by Goodfellow et al. in 2014. In the GAN framework, the generative model is pitted against an adversary: a discriminative model that learns to determine whether a sample is from the model distribution or the data distribution. GAN generates new samples from the same distribution as the training set. In this work, we aim to generate a new dataset that has a different distribution from the training set. In addition, the Jensen-Shannon divergence between the distributions of the generative and training datasets can be controlled by some target $\delta \in [0, 1]$. Our work is motivated by applications in generating new kinds of rice that have similar characteristics as good rice.