Efficient Architecture Search under Leave-One-Subject-Out Evaluation

2026-09-18

AI Summary

本文针对自动疼痛评估系统中神经架构搜索计算量大的问题，提出了一种基于块的、控制泄漏的方法PainNAS，通过在受试者间共享搜索来减少搜索次数，从而提高效率。

Abstract

Deep neural architectures are widely used for signal processing in automated pain assessment systems. However, architecture design has remained largely a manual task despite the potential efficiency benefits of Neural Architecture Search (NAS). Embedding NAS in a Leave-One-Subject-Out (LOSO) evaluation is computationally demanding because a fully nested implementation requires $N$ independent architecture searches and, assuming approximately linear training cost, scales as $\mathcal{O}(N^2)$. We propose a block-based, leakage-controlled approach that shares NAS runs between subjects, reducing the number of searches from $N$ to $B$, where $B \ll N$, dubbed PainNAS. On the BioVid Heat Pain dataset, PainNAS yields comparable subject-level accuracy with substantially fewer parameters and FLOPs.

Problem

• Neural Architecture Search
• Leave-One-Subject-Out
• Pain Assessment
• Computational Cost
• Efficiency

Innovation

• Neural Architecture Search
• Leave-One-Subject-Out
• Block-based Approach
• Leakage Control