速率-失真-欺骗权衡

中文摘要 AI 辅助

传统上，在失真和感知这两个主要约束下研究给定随机变量的最优压缩率问题。失真约束确保重建与随机变量的观测实现的保真度，感知约束保证重建接近感兴趣随机变量分布的样本。本文探讨一种重建可能性，即重建样本在保真度上与原始随机变量实现相符，同时类似来自不同目标分布的样本，我们将此准则称为欺骗约束，并找到速率-失真与欺骗的基本权衡。

英文摘要

The problem of finding the optimal compression rate for a given random variable has been traditionally studied under two main constraints: distortion and perception. The distortion constraint enforces the fidelity of our reconstruction with respect to the observed realization of the random variable, while the perception constraint ensures that the reconstruction is close to a sample from the distribution of the random variable of interest. In this work, we explore the possibility of reconstruction, such that the reconstructed sample is still within a desired fidelity level with our original realization of the random variable, but at the same time, it resembles a sample from a different target distribution. We term this criterion as the deception constraint and find the fundamental tradeoffs of rate-distortion and deception.

发表机构

• University of Maryland, College Park（马里兰大学帕克分校）
• The Ohio State University（俄亥俄州立大学）

机构由 AI 辅助整理，请以论文原文为准。