Robust NAS under adversarial training: benchmark, theory, and beyond
Yongtao Wu Fanghui Liu Carl-Johann Simon-Gabriel Grigorios G Chrysos Volkan Cevher
Recent developments in neural architecture search (NAS) emphasize the
significance of considering robust architectures against malicious data.
However, there is a notable absence of benchmark evaluations and theoretical
guarantees for searching these robust architectures, especially when
adversarial training is considered. In this work, we aim to address these two
challenges, making twofold contributions. First, we release a comprehensive
data set that encompasses both clean accuracy and robust accuracy for a vast
array of adversarially trained networks from the NAS-Bench-201 search space on
image datasets. Then, leveraging the neural tangent kernel (NTK) tool from deep
learning theory, we establish a generalization theory for searching
architecture in terms of clean accuracy and robust accuracy under
multi-objective adversarial training. We firmly believe that our benchmark and
theoretical insights will significantly benefit the NAS community through
reliable reproducibility, efficient assessment, and theoretical foundation,
particularly in the pursuit of robust architectures.