Image generation is a long-standing problem in the machine learning and computer vision areas. In order to generate images with high diversity, we propose a novel model called generative adversarial networks with mixture of t-distributions noise (tGANs). In tGANs, the latent generative space is formulated using a mixture of t-distributions. Particularly, the parameters of the components in the mixture of t-distributions can be learned along with others in the model. To improve the diversity of the generated images in each class, each noise vector and a class codeword are concatenated as the input of the generator of tGANs. In addition, a classification loss is added to both the generator and the discriminator losses to strengthen their performances. We have conducted extensive experiments to compare tGANs with a state-of-the-art pixel by pixel image generation approach, pixelCNN, and related GAN-based models. The experimental results and statistical comparisons demonstrate that tGANs perform significantly better than pixleCNN and related GAN-based models for diverse image generation.

中文翻译：

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具有t分布噪声混合的生成对抗网络，可用于生成多种图像。

在机器学习和计算机视觉领域，图像生成是一个长期存在的问题。为了生成具有高度多样性的图像，我们提出了一种新的模型，称为混合对抗网络，该网络具有t分布噪声（tGAN）的混合。在tGAN中，潜在的生成空间是使用t分布的混合物制定的。特别是，可以与模型中的其他分布一起了解t分布混合中各成分的参数。为了改善每个类别中所生成图像的多样性，将每个噪声矢量和一个类别码字串联起来，作为tGAN生成器的输入。另外，分类损失被添加到生成器损失和鉴别器损失两者上，以增强其性能。我们进行了广泛的实验，将tGAN与最新的逐像素图像生成方法，pixelCNN和相关的基于GAN的模型进行比较。实验结果和统计比较表明，对于多种图像生成，tGAN的性能明显优于pixleCNN和相关的基于GAN的模型。