Tiny adversarial multi-objective one-shot neural architecture search
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.
National Natural Science Foundation of China["62136003","61972188","62122035","62206122","62103150"] ; China Postdoctoral Science Foundation[2021M691012]
|WOS Research Area|
Computer Science, Artificial Intelligence
|WOS Accession No|
Cited Times [WOS]:0
|Document Type||Journal Article|
|Department||Department of Computer Science and Engineering|
1.Department of Computer Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
2.Department of Computer Science,University of Surrey,Guildford,Surrey,GU2 7XH,United Kingdom
3.Institute of Intelligent Manufacturing,Nanjing Tech University,Nanjing,211816,China
4.School of Engineering Science,University of Chinese Academy of Sciences,Beijing,China
5.Department of Computer Science,Southern University of Science and Technology,Shenzhen,518055,China
6.Faculty of Technology,Bielefeld University,Bielefeld,33619,Germany
7.Department of Computer Science,University of Surrey,Guildford,GU2 7XH,United Kingdom
|First Author Affilication||Department of Computer Science and Engineering|
|First Author's First Affilication||Department of Computer Science and Engineering|
Xie，Guoyang,Wang，Jinbao,Yu，Guo,et al. Tiny adversarial multi-objective one-shot neural architecture search[J]. Complex and Intelligent Systems,2023,9(6).
Xie，Guoyang,Wang，Jinbao,Yu，Guo,Lyu，Jiayi,Zheng，Feng,&Jin，Yaochu.(2023).Tiny adversarial multi-objective one-shot neural architecture search.Complex and Intelligent Systems,9(6).
Xie，Guoyang,et al."Tiny adversarial multi-objective one-shot neural architecture search".Complex and Intelligent Systems 9.6(2023).
|Files in This Item:||There are no files associated with this item.|
|Recommend this item|
|Export to Endnote|
|Export to Excel|
|Export to Csv|
|Similar articles in Google Scholar|
|Similar articles in Baidu Scholar|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.