AI 中文总结
本文揭示任务感知压缩中自然存在的感知约束，通过匹配目标分布解决分类器决策边界不匹配问题，实现速率最小化并提升分类效用。
AI 中文摘要
随着神经压缩器的最新进展，将感知约束明确纳入压缩方案设计已引起广泛关注。传统上，这些感知约束确保重建的分布不会显著偏离源的分布，从而证明重建的感知质量。在这项工作中，我们揭示了任务感知压缩中自然存在的若干感知约束。具体而言，我们考虑一个主要任务是重建、次要任务是分类（即统计检验）的问题。我们在可获得的不同领域信息水平下研究该问题，并讨论如何利用自然涌现的感知约束来设计速率最小的压缩方案，同时最大化我们次要任务的效用。我们证明，在此设置中，如果分类器的决策边界对于我们的源分布定义不当（不匹配），那么匹配到目标分布会提高我们的分类准确性。
英文摘要
With the recent advancements of neural compressors, explicitly incorporating perception constraints into the design of compression schemes has gained significant attention. Traditionally, these perception constraints ensure that the distribution of the reconstruction does not significantly deviate from the distribution of the source, thus attesting to the perceptual quality of the reconstruction. In this work, we uncover several perception constraints that are naturally present in task-aware compression. In particular, we consider a problem where the primary task is reconstruction and the secondary task is classification (i.e., a statistical test). We study this problem at varying levels of domain information available to us and discuss how to utilize the naturally emerging perception constraints to design rate-minimal compression schemes that also maximize the utility of our secondary task. We show that in this setting, if the decision boundaries of the classifier are ill-defined (mismatch) for our source distribution, then matching onto a target distribution enhances our classification accuracy.
