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1.9 KiB
1.9 KiB
PNASNet
Progressive Neural Architecture Search, or PNAS, is a method for learning the structure of convolutional neural networks (CNNs). It uses a sequential model-based optimization (SMBO) strategy, where we search the space of cell structures, starting with simple (shallow) models and progressing to complex ones, pruning out unpromising structures as we go.
{% include 'code_snippets.md' %}
How do I train this model?
You can follow the timm recipe scripts for training a new model afresh.
Citation
@misc{liu2018progressive,
title={Progressive Neural Architecture Search},
author={Chenxi Liu and Barret Zoph and Maxim Neumann and Jonathon Shlens and Wei Hua and Li-Jia Li and Li Fei-Fei and Alan Yuille and Jonathan Huang and Kevin Murphy},
year={2018},
eprint={1712.00559},
archivePrefix={arXiv},
primaryClass={cs.CV}
}