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author    = {Esteban Real and
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               Quoc V. Le and
               Alexey Kurakin},
  editor    = {Doina Precup and
               Yee Whye Teh},
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@inproceedings{zoph2017neural,
author    = {Barret Zoph and
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@inproceedings{tan2019mnasnet,
  author    = {Mingxing Tan and
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               Ruoming Pang and
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               Mark Sandler and
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  title     = {MnasNet: Platform-Aware Neural Architecture Search for Mobile},
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  year      = {2019},
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@inproceedings{liu2018darts,
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  year = {2019},
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@inproceedings{madry2017towards,
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               Peter Vajda and
               Yangqing Jia and
               Kurt Keutzer},
  title     = {FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search},
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  year      = {2019},
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@inproceedings{pang2019rethinking,
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               Jun Zhu},
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  year      = {2020},
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  title     = {Towards Robust Neural Networks via Random Self-ensemble},
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@inproceedings{zhai2019s4l,
  title={S4l: Self-supervised semi-supervised learning},
  author={Zhai, Xiaohua and Oliver, Avital and Kolesnikov, Alexander and Beyer, Lucas},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision},
  pages={1476--1485},
  year={2019}
}

@inproceedings{mok2021advrush,
  title={AdvRush: Searching for adversarially robust neural architectures},
  author={Mok, Jisoo and Na, Byunggook and Choe, Hyeokjun and Yoon, Sungroh},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={12322--12332},
  year={2021}
}

@Article{xie2021tiny,
author={Xie, Guoyang
and Wang, Jinbao
and Yu, Guo
and Lyu, Jiayi
and Zheng, Feng
and Jin, Yaochu},
title={Tiny adversarial multi-objective one-shot neural architecture search},
journal={Complex {\&} Intelligent Systems},
year={2023},
month={Jul},
day={11},
abstract={The widely employed tiny neural networks (TNNs) in mobile devices are vulnerable to adversarial attacks. However, more advanced research on the robustness of TNNs is highly in demand. This work focuses on improving the robustness of TNNs without sacrificing the model's accuracy. To find the optimal trade-off networks in terms of the adversarial accuracy, clean accuracy, and model size, we present TAM-NAS, a tiny adversarial multi-objective one-shot network architecture search method. First, we build a novel search space comprised of new tiny blocks and channels to establish a balance between the model size and adversarial performance. Then, we demonstrate how the supernet facilitates the acquisition of the optimal subnet under white-box adversarial attacks, provided that the supernet significantly impacts the subnet's performance. Concretely, we investigate a new adversarial training paradigm by evaluating the adversarial transferability, the width of the supernet, and the distinction between training subnets from scratch and fine-tuning. Finally, we undertake statistical analysis for the layer-wise combination of specific blocks and channels on the first non-dominated front, which can be utilized as a design guideline for the design of TNNs.},
}
