• Corpus ID: 54438210

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

@article{Cai2018ProxylessNASDN,
  title={ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware},
  author={Han Cai and Ligeng Zhu and Song Han},
  journal={ArXiv},
  year={2018},
  volume={abs/1812.00332}
}
Neural architecture search (NAS) has a great impact by automatically designing effective neural network architectures. However, the prohibitive computational demand of conventional NAS algorithms (e.g. $10^4$ GPU hours) makes it difficult to \emph{directly} search the architectures on large-scale tasks (e.g. ImageNet). Differentiable NAS can reduce the cost of GPU hours via a continuous representation of network architecture but suffers from the high GPU memory consumption issue (grow linearly… 

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