论文标题
生成对抗网：我们可以仅基于一个培训集生成一个新的数据集吗？
Generative Adversarial Nets: Can we generate a new dataset based on only one training set?
论文作者
论文摘要
生成对抗网络（GAN）是Goodfellow等人设计的一类机器学习框架。在2014年。在GAN框架中，生成模型与对手：一个学会确定样本是来自模型分布还是数据分布的歧视模型。 GAN从与培训集相同的分布中生成了新样本。在这项工作中，我们旨在生成一个与培训集不同的新数据集。此外，可以控制某些目标$δ\在[0，1] $中的某些目标$δ\可以控制生成数据集和培训数据集之间的詹森 - 香农差异。我们的工作是由应用与优质大米相似的新型大米时的应用激发的。
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 $δ\in [0, 1]$. Our work is motivated by applications in generating new kinds of rice that have similar characteristics as good rice.
