机器学习时代的分布式压缩：近期进展综述
Distributed Compression in the Era of Machine Learning: A Review of Recent Advances
AI总结：
本文综述了面向抽象源和图像的学习型分布式压缩技术的近期进展，重点讨论了接近信息论界限且具可解释性的方法，并指出了该领域未解决的挑战。
摘要：
从相机阵列到传感器网络的许多应用都需要对相关数据进行高效的压缩和处理，这些数据通常是分布式采集的。尽管分布式压缩的信息论基础已得到充分研究，但理论在面向实际的应用中所产生的影响至今仍较为有限。随着基于学习的技术兴起，数据压缩领域正经历一场变革，机器学习正成为获取分布式压缩长期承诺收益的重要工具。本文综述了抽象源和图像的学习型分布式压缩技术这一广泛领域的近期贡献。特别地，我们讨论了能够提供可解释结果且运行接近信息论界限的方法。我们还强调了尚未解决的研究挑战，旨在激发对学习型分布式压缩领域的新兴趣与进展。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
