MixerFlow: MLP-Mixer meets Normalising Flows
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| _version_ | 1866917706487300096 |
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| author | English, Eshant Kirchler, Matthias Lippert, Christoph |
| author_facet | English, Eshant Kirchler, Matthias Lippert, Christoph |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_16777 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | MixerFlow: MLP-Mixer meets Normalising Flows English, Eshant Kirchler, Matthias Lippert, Christoph Machine Learning Computer Vision and Pattern Recognition 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. |
| title | MixerFlow: MLP-Mixer meets Normalising Flows |
| topic | Machine Learning Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.16777 |