Flow-based generative models (flow models) are conceptually attractive due to tractability of the exact log-likelihood and the exact latent-variable inference. In order to generate sharper images and extend the Gaussian prior of Flow models to other discrete forms, we propose an adversarial non-volume preserving flow model with Boltzmann priors (ANVP) for modeling complex high-dimensional densities. In order to generate sharper images, an ANVP model introduces an adversarial regularizer into the loss function to penalize the condition that it places a high probability in regions where the training data distribution has a low density. Moreover, we show that the Gaussian prior can be extended to other forms such as the Boltzmann prior in the proposed ANVP model, and we use multi-scale transformations and Boltzmann priors to model the data distribution. The experiments show that proposed model is effective in image generation task.

中文翻译：

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玻尔兹曼先验的对抗非容量保流模型

基于流的生成模型（流模型）在概念上很有吸引力，这是因为精确的对数似然性和精确的潜在变量推断具有易处理性。为了生成更清晰的图像并将流模型的高斯先验扩展到其他离散形式，我们提出了一种使用Boltzmann先验（ANVP）的对抗性非体积保留流模型来对复杂的高维密度进行建模。为了生成更清晰的图像，ANVP模型将对抗性正则化函数引入损失函数，以惩罚在训练数据分布密度较低的区域中概率较高的条件。此外，我们证明了高斯先验可以扩展到其他形式，例如在提出的ANVP模型中的玻耳兹曼先验，我们使用多尺度变换和玻尔兹曼先验模型对数据分布进行建模。实验表明，该模型在图像生成任务中是有效的。