Effective, Efficient and Robust Neural Architecture Search
- Publisher:
- IEEE
- Publication Type:
- Conference Proceeding
- Citation:
- Proceedings of the International Joint Conference on Neural Networks, 2022, 2022-July, pp. 1-8
- Issue Date:
- 2022-01-01
Embargoed
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| Effective, Efficient and Robust Neural Architecture Search.pdf | Accepted version | 435.82 kB |
Copyright Clearance Process
- Recently Added
- In Progress
- Open Access
This item is currently unavailable due to the publisher's embargo.
Designing neural network architecture for embedded devices is practical but challenging because the models are expected to be not only accurate but also enough lightweight and robust. However, it is challenging to balance those trade-offs manually because of the large search space. To solve this problem, we propose an Effective, Efficient, and Robust Neural Architecture Search (E2RNAS) method to automatically search a neural network architecture that balances the performance, robustness, and resource consumption. Unlike previous studies, the objective function of the proposed E2RNAS method is formulated as a multi-objective bi-level optimization problem with the upper-level subproblem as a multi-objective optimization problem that considers the performance, robustness, and resource consumption. To solve the proposed objective function, we integrate the multiple-gradient descent algorithm, a widely studied gradient-based multi-objective optimization algorithm, with the bi-level optimization. Experiments on benchmark datasets show that the proposed E2RNAS method can find robust architecture with low resource consumption and comparable classification accuracy.
Please use this identifier to cite or link to this item: