Machine learning has had a major impact on data compression over the last decade and opened up many new theoretical and applied fields of inquiry. This paper describes one such direction -- relative entropy coding -- which focuses on constructing stochastic codes, mainly as an alternative to quantisation and entropy coding in lossy source coding. Our primary aim is to provide a broad overview of the topic, with an emphasis on the computational and practical aspects currently missing from the literature. Our goal is threefold: for the curious reader, we aim to provide an intuitive picture of the field and convince them that relative entropy coding is a simple yet exciting emerging field in data compression research. For a reader interested in applied research on lossy data compression, we provide an account of the most salient contemporary applications. Finally, for the reader who has heard of relative entropy coding but has never been quite sure what it is or how the algorithms fit together, we hope to illustrate how simple and elegant the underlying constructions are.

翻译：机器学习在过去十年中对数据压缩产生了重大影响，并开辟了许多新的理论和应用研究领域。本文描述其中一个方向——相对熵编码——其重点关注构建随机码，主要作为有损信源编码中量化和熵编码的替代方案。我们的首要目标是提供该主题的广泛概述，着重于当前文献中缺失的计算和实际方面。我们的目标有三重：对于好奇的读者，我们希望提供该领域的直观图景，并让他们相信相对熵编码是数据压缩研究中一个简单而令人兴奋的新兴领域。对于有损数据压缩应用研究的读者，我们提供了最显著当代应用的说明。最后，对于听说过相对熵编码但始终不太确定它是什么或算法如何组合在一起的读者，我们希望说明其底层结构有多么简单和优雅。