论文标题
通过多尺度自回旋先验的流量正常
Normalizing Flows with Multi-Scale Autoregressive Priors
论文作者
论文摘要
基于流量的生成模型是一系列重要的精确推理模型，这些模型接受图像合成的有效推理和采样。由于对流量层设计的效率约束，例如分裂耦合流量层，其中大约一半的像素不会进行进一步的转换，与依赖有条件的像素智能生成的自回旋模型相比，它们对长距离数据依赖性建模的表达有限。在这项工作中，我们通过通过多尺度自回旋先验（MAR）在其潜在空间中引入频道依赖性（MAR）来提高基于流的模型的代表性。我们的MAR先前的具有分裂耦合流层（MAR-SCF）的模型可以更好地捕获复杂多模式数据中的依赖项。最终的模型可实现MNIST，CIFAR-10和Imagenet的最新密度估计结果。此外，我们表明MAR-SCF允许提高图像生成质量，与最先进的基于流动的模型相比，FID和Inception分数的提高。
Flow-based generative models are an important class of exact inference models that admit efficient inference and sampling for image synthesis. Owing to the efficiency constraints on the design of the flow layers, e.g. split coupling flow layers in which approximately half the pixels do not undergo further transformations, they have limited expressiveness for modeling long-range data dependencies compared to autoregressive models that rely on conditional pixel-wise generation. In this work, we improve the representational power of flow-based models by introducing channel-wise dependencies in their latent space through multi-scale autoregressive priors (mAR). Our mAR prior for models with split coupling flow layers (mAR-SCF) can better capture dependencies in complex multimodal data. The resulting model achieves state-of-the-art density estimation results on MNIST, CIFAR-10, and ImageNet. Furthermore, we show that mAR-SCF allows for improved image generation quality, with gains in FID and Inception scores compared to state-of-the-art flow-based models.
