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.

通过使用双射变换将复杂密度转化为简单密度的统计模型，我们提出了一种名为 MixerFlow 的新型架构，基于 MLP-Mixer 架构，进一步统一生成和判别建模架构。MixerFlow 提供了一种用于流模型的有效权重共享机制，并在固定的计算预算下，在图像数据集上展示了更好的密度估计，随着图像分辨率的增加，缩放效果良好，使 MixerFlow 成为 Glow-based 架构的一个强大而简单的替代方案。我们还显示 MixerFlow 提供比 Glow-based 架构更丰富的嵌入。