RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

• URL: http://arxiv.org/abs/2109.05211v2
• Date: Wed, 15 Sep 2021 08:15:57 GMT
• Title: RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
• Authors: Shiyu Tang and Ruihao Gong and Yan Wang and Aishan Liu and Jiakai Wang and Xinyun Chen and Fengwei Yu and Xianglong Liu and Dawn Song and Alan Yuille and Philip H.S. Torr and Dacheng Tao
• Abstract summary: Deep neural networks (DNNs) are vulnerable to adversarial noises. There are no comprehensive studies of how architecture design and training techniques affect robustness. We propose the first comprehensiveness investigation benchmark on ImageNet.
• Score: 170.3297213957074
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep neural networks (DNNs) are vulnerable to adversarial noises, which motivates the benchmark of model robustness. Existing benchmarks mainly focus on evaluating the defenses, but there are no comprehensive studies of how architecture design and general training techniques affect robustness. Comprehensively benchmarking their relationships will be highly beneficial for better understanding and developing robust DNNs. Thus, we propose RobustART, the first comprehensive Robustness investigation benchmark on ImageNet (including open-source toolkit, pre-trained model zoo, datasets, and analyses) regarding ARchitecture design (44 human-designed off-the-shelf architectures and 1200+ networks from neural architecture search) and Training techniques (10+ general techniques, e.g., data augmentation) towards diverse noises (adversarial, natural, and system noises). Extensive experiments revealed and substantiated several insights for the first time, for example: (1) adversarial training largely improves the clean accuracy and all types of robustness for Transformers and MLP-Mixers; (2) with comparable sizes, CNNs > Transformers > MLP-Mixers on robustness against natural and system noises; Transformers > MLP-Mixers > CNNs on adversarial robustness; (3) for some light-weight architectures (e.g., EfficientNet, MobileNetV2, and MobileNetV3), increasing model sizes or using extra training data cannot improve robustness. Our benchmark http://robust.art/ : (1) presents an open-source platform for conducting comprehensive evaluation on diverse robustness types; (2) provides a variety of pre-trained models with different training techniques to facilitate robustness evaluation; (3) proposes a new view to better understand the mechanism towards designing robust DNN architectures, backed up by the analysis. We will continuously contribute to building this ecosystem for the community.

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