arXiv AI By Richard Goldman, Varun Komperla, Thomas Ploetz, Harish Haresamudram

Models Got Talent: Identifying High Performing Wearable Human Activity Recognition Models Without Training

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The paper investigates the use of Zero Cost Proxies (ZCPs) to identify high‑performing wearable Human Activity Recognition (HAR) models without full training. Eight ZCPs were evaluated across six benchmark HAR datasets, showing that the top‑predicted architectures achieve performance within 7% of fully trained models, and training the top‑10 predictions reaches within 2% of full training. This demonstrates that ZCPs can significantly reduce computational costs while maintaining competitive accuracy in sensor‑based HAR tasks.

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