Overview

PaddleSlim提供了4种网络结构搜索的方法：基于模拟退火进行网络结构搜索、基于强化学习进行网络结构搜索、基于梯度进行网络结构搜索和Once-For-All。

参考文献

[1] H. Cai, C. Gan, T. Wang, Z. Zhang, and S. Han. Once for all: Train one network and specialize it for efficient deployment. In International Conference on Learning Representations, 2020.
[2] Pham, H.; Guan, M. Y.; Zoph, B.; Le, Q. V.; and Dean, J. 2018. Efficient neural architecture search via parameter sharing. arXiv preprint arXiv:1802.03268.
[3] Zoph B, Vasudevan V, Shlens J, et al. Learning transferable architectures for scalable image recognition[J]. arXiv preprint arXiv:1707.07012, 2017, 2(6).
[4] Mingxing Tan, Bo Chen, Ruoming Pang, Vijay Vasudevan, and Quoc V Le. Mnasnet: Platform-aware neural architecture search for mobile. arXiv preprint arXiv:1807.11626, 2018.
[5] H Liu, K Simonyan, Y Yang. Darts: Differentiable architecture search. arXiv preprint arXiv:1806.09055, 2018.
[6] Xu, Y., Xie, L., Zhang, X., Chen, X., Qi, G.J., Tian, Q., Xiong, H.: PCDARTS: Partial Channel Connections for Memory-efficient Differentiable Architecture Search. In: International Conference on Learning Representations (2020)
[7] Han Cai, Ligeng Zhu, and Song Han. ProxylessNAS: Direct neural architecture search on target task and hardware. In ICLR, 2019. URL https://arxiv.org/pdf/1812.00332.pdf. 3, 5, 6, 7, 8