论文标题
有效，高效和强大的神经架构搜索
Effective, Efficient and Robust Neural Architecture Search
论文作者
论文摘要
对抗攻击的最新进展表明，神经建筑搜索（NAS）搜索的深神经网络的脆弱性。尽管NAS方法可以找到具有最先进性能的网络体系结构，但在NAS中通常会忽略对抗性的鲁棒性和资源约束。为了解决这个问题，我们提出了一种有效，有效且健壮的神经体系结构搜索（E2RNA）方法，以考虑到绩效，鲁棒性和资源约束来搜索神经网络体系结构。提出的E2RNA方法的目标函数被公正为双级多目标优化问题，上层问题是一个多目标优化问题，这与现有的NAS方法不同。为了求解提出的目标函数，我们将多梯度下降算法（一种广泛研究的基于梯度的多目标优化算法）与BI级优化相结合。基准数据集上的实验表明，提出的E2RNA方法可以找到具有优化模型大小和可比分类精度的对抗性稳健体系结构。
Recent advances in adversarial attacks show the vulnerability of deep neural networks searched by Neural Architecture Search (NAS). Although NAS methods can find network architectures with the state-of-the-art performance, the adversarial robustness and resource constraint are often ignored in NAS. To solve this problem, we propose an Effective, Efficient, and Robust Neural Architecture Search (E2RNAS) method to search a neural network architecture by taking the performance, robustness, and resource constraint into consideration. The objective function of the proposed E2RNAS method is formulated as a bi-level multi-objective optimization problem with the upper-level problem as a multi-objective optimization problem, which is different from existing NAS methods. 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 adversarially robust architectures with optimized model size and comparable classification accuracy.
