English

Prune and Replace NAS

Machine Learning 2021-04-23 v2 Machine Learning

Abstract

While recent NAS algorithms are thousands of times faster than the pioneering works, it is often overlooked that they use fewer candidate operations, resulting in a significantly smaller search space. We present PR-DARTS, a NAS algorithm that discovers strong network configurations in a much larger search space and a single day. A small candidate operation pool is used, from which candidates are progressively pruned and replaced with better performing ones. Experiments on CIFAR-10 and CIFAR-100 achieve 2.51% and 15.53% test error, respectively, despite searching in a space where each cell has 150 times as many possible configurations than in the DARTS baseline. Code is available at https://github.com/cogsys-tuebingen/prdarts

Keywords

Cite

@article{arxiv.1906.07528,
  title  = {Prune and Replace NAS},
  author = {Kevin Alexander Laube and Andreas Zell},
  journal= {arXiv preprint arXiv:1906.07528},
  year   = {2021}
}

Comments

9 pages, 3 figures, 3 tables reworked, accepted at the ICMLA 2019

R2 v1 2026-06-23T09:56:49.707Z