Many applications from camera arrays to sensor networks require efficient compression and processing of correlated data, which in general is collected in a distributed fashion. While information-theoretic foundations of distributed compression are well investigated, the impact of theory in practice-oriented applications to this day has been somewhat limited. As the field of data compression is undergoing a transformation with the emergence of learning-based techniques, machine learning is becoming an important tool to reap the long-promised benefits of distributed compression. In this paper, we review the recent contributions in the broad area of learned distributed compression techniques for abstract sources and images. In particular, we discuss approaches that provide interpretable results operating close to information-theoretic bounds. We also highlight unresolved research challenges, aiming to inspire fresh interest and advancements in the field of learned distributed compression.

翻译：从相机阵列到传感器网络的众多应用都需要对相关数据进行高效压缩和处理，而这些数据通常以分布式方式采集。虽然分布式压缩的信息理论基础已得到充分研究，但理论对实践导向型应用的影响至今仍较为有限。随着基于学习的技术兴起，数据压缩领域正在经历转型，机器学习正成为实现分布式压缩长期承诺的重要工具。本文综述了面向抽象信源与图像的分布式压缩学习技术领域的近期研究成果。我们重点讨论了能够逼近信息理论界限且结果可解释的方法，并指出了尚未解决的研究挑战，以期激发分布式压缩学习领域的新兴趣与进展。