In this paper, we present a new class of invertible transformations. We indicate that many well-known invertible tranformations in reversible logic and reversible neural networks could be derived from our proposition. Next, we propose two new coupling layers that are important building blocks of flow-based generative models. In the preliminary experiments on toy digit data, we present how these new coupling layers could be used in Integer Discrete Flows (IDF), and that they achieve better results than standard coupling layers used in IDF and RealNVP.

中文翻译：

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基于流的生成模型的通用可逆转换

在本文中，我们提出了一类新的可逆转换。我们指出，可逆逻辑和可逆神经网络中的许多众所周知的可逆变换都可以从我们的命题中得出。接下来，我们提出两个新的耦合层，它们是基于流的生成模型的重要构建块。在有关玩具数字数据的初步实验中，我们介绍了如何在整数离散流（IDF）中使用这些新的耦合层，并且与IDF和RealNVP中使用的标准耦合层相比，它们可以获得更好的结果。