Empirically, we augment CIFAR-10 with 500K unlabeled images sourced from 80 Million Tiny Images and use robust self-training to outperform state-of-the-art robust accuracies by over 5 points in (i) ` 1 robustness against sev- We design two simple but effective methods to promote model robustness based on the critical attacking route. 86.46%: 56.03% ☑ WideResNet-28-10: NeurIPS 2019: 12: Using Pre-Training Can Improve Model Robustness and Uncertainty: 87.11%: 54.92% ☑ WideResNet-28-10: ICML 2019: 13 Bibliographic details on Are Labels Required for Improving Adversarial Robustness? In this paper, we investigate the choice of the target labels for augmented inputs and show how to apply AutoLabelto these existing data augmentation techniques to further improve model’s robustness. arXiv preprint arXiv:1810.00740, 2018. Label-Smoothing and Adversarial Robustness. ... finding that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. The past few years have seen an intense research interest in making models robust to adversarial examples [] Yet despite a wide range of proposed defenses, the state-of-the-art in adversarial robustness is far from satisfactory. Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack sam-ples can significantly disturb the performance of a model. Supported datasets and NN architectures: Many recent methods have proposed to improve adversar-ial robustness by utilizing adversarial training or model distillation, which adds additional procedures to model training. Calibration and Uncertainty Estimates. Are Labels Required for Improving Adversarial Robustness? Are labels required for improving adversarial robustness? These findings open a new avenue for improving adversarial robustness using unlabeled data. Key Takeaways. (sorry, in German only) Betreiben Sie datenintensive Forschung in der Informatik? Neural networks have led to major improvements in image classification but suffer from being non-robust to adversarial changes, unreliable uncertainty estimates on out-distribution samples and their inscrutable black-box decisions. A range of defense techniques have been proposed to improve DNN robustness to adversarial examples, among which adversarial training has been demonstrated to be the most effective. dblp ist Teil eines sich formierenden Konsortiums für eine nationalen Forschungsdateninfrastruktur, und wir interessieren uns für Ihre Erfahrungen. Improving the generalization of adversarial training with domain adaptation. Adversarial Weight Perturbation Helps Robust Generalization: 85.36%: 56.17% × WideResNet-34-10: NeurIPS 2020: 11: Are Labels Required for Improving Adversarial Robustness? [9] Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft. Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. See the paper for more information about Label-Smoothing and a full understanding of the hyperparatemer. robust accuracy using the same number of labels required for achieving high stan-dard accuracy. "Are labels required for improving adversarial robustness?," in Advances in Neural Information Processing Systems, 2019. This approach improves the state-of-the-art on CIFAR-10 by 4% against the strongest known attack. Adversarial robustness: From selfsupervised pre-training to … 5.1. Model adversarial robustness enhancement. technique aiming for improving model’s adversarial robustness. arXiv preprint arXiv:1905.13725, 2019. 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