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

• URL: http://arxiv.org/abs/2210.06005v1
• Date: Wed, 12 Oct 2022 08:22:12 GMT
• Title: Generative Adversarial Nets: Can we generate a new dataset based on only one training set?
• Authors: Lan V. Truong
• Abstract summary: A generative adversarial network (GAN) is a class of machine learning frameworks designed by Goodfellow et al. 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.
• Score: 16.3460693863947
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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