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

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.

Machine Learning (cs.LG)

arXiv:2609.21457 [cs.LG]

(or arXiv:2609.21457v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2609.21457 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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