Remarkable progress has been achieved in synthesizing photo-realistic images with generative adversarial neural networks (GANs). Recently, GANs are utilized as the training sample generator when obtaining or storing real training data is expensive even infeasible. However, traditional GANs generated images are not as informative as the real training samples when being used to train deep neural networks. In this paper, we propose a novel method to synthesize Informative Training samples with GAN (IT-GAN). Specifically, we freeze a pre-trained GAN model and learn the informative latent vectors that corresponds to informative training samples. The synthesized images are required to preserve information for training deep neural networks rather than visual reality or fidelity. Experiments verify that the deep neural networks can learn faster and achieve better performance when being trained with our IT-GAN generated images. We also show that our method is a promising solution to dataset condensation problem.

翻译：在将光现实图像与基因对抗神经网络(GANs)相结合方面取得了显著进展。最近,在获取或储存实际培训数据时,GANs作为培训样本生成器被使用,成本甚至不高,甚至不可行。然而,传统GANs生成的图像在用于培训深层神经网络时并没有像实际培训样本那样信息丰富。在本文中,我们提出了一种与GAN(IT-GAN)结合信息培训样本的新方法。具体地说,我们冻结了预先培训的GAN模型,并学习了与信息培训样本相匹配的信息潜在矢量。合成图像需要保存信息,用于培训深层神经网络,而不是视觉现实或真实性。实验证实深层神经网络在接受IT-GAN生成图像培训时能够更快地学习并取得更好的业绩。我们还表明,我们的方法是解决数据集集中问题的有希望的方法。