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

Heinke Hihn; Friedhelm Schwenker

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arXiv · 2609.21457

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

Heinke Hihn·Friedhelm Schwenker

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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.

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Heinke Hihn, Friedhelm Schwenker. 2026-09-21. Efficient Architecture Search under Leave-One-Subject-Out Evaluation. https://arxiv.org/abs/2609.21457

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