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

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.
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.
Generative Adversarial Nets: Can we generate a new dataset based on only one training set?
http://arxiv.org/abs/2210.06005v1