Abstract
A promising alternative to the computationally expensive Neural Architecture Search (NAS) involves the development of \textit{Zero Cost Proxies (ZCPs)}, which correlate well to 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.
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URL
https://arxiv.org/abs/2511.06157
