# β-DARTS: Beta-Decay Regularization for Differentiable Architecture Search

@article{Ye2022DARTSBR,
title={$\beta$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search},
author={Peng Ye and Baopu Li and Yikang Li and Tao Chen and Jiayuan Fan and Wanli Ouyang},
journal={ArXiv},
year={2022},
volume={abs/2203.01665}
}
• Published 3 March 2022
• Computer Science
• ArXiv
Neural Architecture Search (NAS) has attracted increasingly more attention in recent years because of its capability to design deep neural network automatically. Among them, differential NAS approaches such as DARTS, have gained popularity for the search efficiency. However, they suffer from two main issues, the weak robustness to the performance collapse and the poor generalization ability of the searched architectures. To solve these two problems, a simple-but-efficient regularization method…
2 Citations

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