NEURAL ARCHITECTURE DESIGN AND ROBUSTNESS: A DATASET

Abstract

Deep learning models have proven to be successful in a wide range of machine learning tasks. Yet, they are often highly sensitive to perturbations on the input data which can lead to incorrect decisions with high confidence, hampering their deployment for practical use-cases. Thus, finding architectures that are (more) robust against perturbations has received much attention in recent years. Just like the search for well-performing architectures in terms of clean accuracy, this usually involves a tedious trial-and-error process with one additional challenge: the evaluation of a network's robustness is significantly more expensive than its evaluation for clean accuracy. Thus, the aim of this paper is to facilitate better streamlined research on architectural design choices with respect to their impact on robustness as well as, for example, the evaluation of surrogate measures for robustness. We therefore borrow one of the most commonly considered search spaces for neural architecture search for image classification, NAS-Bench-201, which contains a manageable size of 6 466 non-isomorphic network designs. We evaluate all these networks on a range of common adversarial attacks and corruption types and introduce a database on neural architecture design and robustness evaluations. We further present three exemplary use cases of this dataset, in which we (i) benchmark robustness measurements based on Jacobian and Hessian matrices for their robustness predictability, (ii) perform neural architecture search on robust accuracies, and (iii) provide an initial analysis of how architectural design choices affect robustness. We find that carefully crafting the topology of a network can have substantial impact on its robustness, where networks with the same parameter count range in mean adversarial robust accuracy from 20% -41%. Code and data is available at http://robustness.vision/.

1. INTRODUCTION

One factor in the ever-improving performance of deep neural networks is based on innovations in architecture design. The starting point was the unprecedented result of AlexNet (Krizhevsky et al., 2012) on the visual recognition challenge ImageNet (Deng et al., 2009) . Since then, the goal is to find better performing models, surpassing human performance. However, human design of new better performing architectures requires a huge amount of trial-and-error and a good intuition, such that the automated search for new architectures (NAS) receives rapid and growing interest (Zoph & Le, 2017; Real et al., 2017; Ying et al., 2019; Dong & Yang, 2020) . The release of tabular benchmarks (Ying et al., 2019; Dong & Yang, 2020) led to a research change; new NAS methods can be evaluated in a transparent and reproducible manner for better comparison. The rapid growth in NAS research with the main focus on finding new architecture designs with ever-better performance is recently accompanied by the search for architectures that are robust against adversarial attacks and corruptions. This is important, since image classification networks can be easily fooled by adversarial attacks crafted by already light perturbations on the image data, which are invisible for humans. This leads to false predictions of the neural network with high confidence. Robustness in NAS research combines the objective of high performing and robust architectures (Dong & Yang, 2019; Devaguptapu et al., 2021; Dong et al., 2020a; Hosseini et al., 2021; Mok 

