Multi-shot NAS for Discovering Adversarially Robust Convolutional Neural Architectures at Targeted Capacities
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Convolutional neural networks (CNNs) are vulnerable to adversarial examples, and studies show that increasing the model capacity of an architecture topology (e.g., width expansion) can bring consistent robustness improvements. This reveals a clear robustness-efficiency trade-off that should be considered in architecture design. Recent studies have employed one-shot neural architecture search (NAS) to discover adversarially robust architectures. However, since the capacities of different topologies cannot be easily aligned during the search process, current one-shot NAS methods might favor topologies with larger capacity in the supernet. And the discovered topology might be sub-optimal when aligned to the targeted capacity. This paper proposes a novel multi-shot NAS method to explicitly search for adversarially robust architectures at a certain targeted capacity. Specifically, we estimate the reward at the targeted capacity using interior extra-polation of the rewards from multiple supernets. Experimental results demonstrate the effectiveness of the proposed method. For instance, at the targeted FLOPs of 1560M, the discovered MSRobNet-1560 (clean 84.8 RobNet-free (clean 82.8
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