Normalising flows are statistical models that transform a complex density into a simpler density through the use of bijective transformations enabling both density estimation and data generation from a single model. In the context of image modelling, the predominant choice has been the Glow-based architecture, whereas alternative architectures remain largely unexplored in the research community. In this work, we propose a novel architecture called MixerFlow, based on the MLP-Mixer architecture, further unifying the generative and discriminative modelling architectures. MixerFlow offers an effective mechanism for weight sharing for flow-based models. Our results demonstrate better density estimation on image datasets under a fixed computational budget and scales well as the image resolution increases, making MixeFlow a powerful yet simple alternative to the Glow-based architectures. We also show that MixerFlow provides more informative embeddings than Glow-based architectures.

翻译：归一化流是一种统计模型，通过使用双射变换将复杂密度转换为更简单密度，从而能够从单一模型实现密度估计和数据生成。在图像建模领域，基于Glow的架构一直是主流选择，而其他架构在研究社区中仍未得到充分探索。本文提出了一种名为MixerFlow的新型架构，该架构基于MLP-Mixer架构，进一步统一了生成式与判别式建模架构。MixerFlow为基于流的模型提供了一种有效的权重共享机制。我们的结果表明，在固定计算预算下，该架构在图像数据集上实现了更优的密度估计性能，并且随着图像分辨率提升具有良好的扩展性，使MixerFlow成为基于Glow的架构的一种强大而简单的替代方案。我们还表明，与基于Glow的架构相比，MixerFlow能提供更具信息性的嵌入表示。