Jet: A Modern Transformer-Based Normalizing Flow

• URL: http://arxiv.org/abs/2412.15129v1
• Date: Thu, 19 Dec 2024 18:09:42 GMT
• Title: Jet: A Modern Transformer-Based Normalizing Flow
• Authors: Alexander Kolesnikov, André Susano Pinto, Michael Tschannen,
• Abstract summary: We revisit the design of the coupling-based normalizing flow models by carefully ablating prior design choices. We achieve state-of-the-art quantitative and qualitative performance with a much simpler architecture.
• Score: 62.2573739835562
• License:
• Abstract: In the past, normalizing generative flows have emerged as a promising class of generative models for natural images. This type of model has many modeling advantages: the ability to efficiently compute log-likelihood of the input data, fast generation and simple overall structure. Normalizing flows remained a topic of active research but later fell out of favor, as visual quality of the samples was not competitive with other model classes, such as GANs, VQ-VAE-based approaches or diffusion models. In this paper we revisit the design of the coupling-based normalizing flow models by carefully ablating prior design choices and using computational blocks based on the Vision Transformer architecture, not convolutional neural networks. As a result, we achieve state-of-the-art quantitative and qualitative performance with a much simpler architecture. While the overall visual quality is still behind the current state-of-the-art models, we argue that strong normalizing flow models can help advancing research frontier by serving as building components of more powerful generative models.

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