MixerFlow: MLP-Mixer meets Normalising Flows
This work addresses a bottleneck in generative modeling for researchers by offering a simple yet powerful alternative to Glow-based architectures, though it is incremental in nature.
The authors tackled the problem of limited architectural diversity in normalising flows for image modeling by proposing MixerFlow, a novel architecture based on MLP-Mixer, which achieved comparative or superior density estimation on image datasets and scaled well with increasing image resolution.
Normalising flows are generative 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. %However, the requirement for bijectivity imposes the use of specialised architectures. 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 efficient mechanism for weight sharing for flow-based models. Our results demonstrate comparative or superior density estimation on image datasets and good scaling as the image resolution increases, making MixerFlow a simple yet powerful alternative to the Glow-based architectures. We also show that MixerFlow provides more informative embeddings than Glow-based architectures and can integrate many structured transformations such as splines or Kolmogorov-Arnold Networks.