arXiv 论文：任务感知压缩中的隐式感知约束研究
The Hidden Perception Constraint in Task-Aware Compression
这篇论文讲压缩时顺便做分类怎么更省码率还提准确率，做图像压缩或多任务学习的朋友可以看看理论推导。
arXiv 论文 2609.35684v1 研究任务感知压缩中自然存在的感知约束。作者设定主任务为重建、次任务为分类（即统计检验），并在不同域信息可得性水平下展开分析。论文说明如何利用自然涌现的感知约束，设计码率最小的压缩方案，同时最大化次任务效用。结果显示，当分类器决策边界与源分布不匹配时，向目标分布对齐可提升分类准确率。
The Hidden Perception Constraint in Task-Aware Compression
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.