A promising alternative to the computationally expensive Neural Architecture Search (NAS) involves the development of Zero Cost Proxies (ZCPs), which correlate well with trained performance, but can be computed through a single forward/backward pass on a randomly sampled batch of data. In this paper, we investigate the effectiveness of ZCPs for HAR on six benchmark datasets, and demonstrate that they discover network architectures that obtain within 5% of performance attained by full-scale training involving 1500 randomly sampled architectures. This results in substantial computational savings as high-performing architectures can be discovered with minimal training. Our experiments not only introduce ZCPs to sensor-based HAR, but also demonstrate that they are robust to data noise, further showcasing their suitability for practical scenarios.
Cite
@article{arxiv.2511.06157,
title = {Models Got Talent: Identifying High Performing Wearable Human Activity Recognition Models Without Training},
author = {Richard Goldman and Varun Komperla and Thomas Ploetz and Harish Haresamudram},
journal= {arXiv preprint arXiv:2511.06157},
year = {2025}
}