We design two simple but effective methods to promote model robustness based on the critical attacking route. Improving the generalization of adversarial training with domain adaptation. arXiv preprint arXiv:1905.13725, 2019. Many recent methods have proposed to improve adversar-ial robustness by utilizing adversarial training or model distillation, which adds additional procedures to model training. Recent work points towards sample complexity as a possible reason for the small gains in robustness: Schmidt et al. Adversarial training is often formulated as a min-max optimization problem, with the inner maximization for generating adversarial examples. Label-Smoothing and Adversarial Robustness. These findings open a new avenue for improving adversarial robustness using unlabeled data. 5.1. Bibliographic details on Are Labels Required for Improving Adversarial Robustness? "Are labels required for improving adversarial robustness?," in Advances in Neural Information Processing Systems, 2019. [9] Chuanbiao Song, Kun He, Liwei Wang, and John E Hopcroft. Are Labels Required for Improving Adversarial Robustness? 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. Adversarial Weight Perturbation Helps Robust Generalization: 85.36%: 56.17% × WideResNet-34-10: NeurIPS 2020: 11: Are Labels Required for Improving Adversarial Robustness? Adversarial robustness: From selfsupervised pre-training to … technique aiming for improving model’s adversarial robustness. dblp ist Teil eines sich formierenden Konsortiums für eine nationalen Forschungsdateninfrastruktur, und wir interessieren uns für Ihre Erfahrungen. Motivated by our observations, in this section, we try to improve model robustness by constraining the behaviors of critical attacking neurons, e.g., gradients, propagation process. 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- Model adversarial robustness enhancement. See the paper for more information about Label-Smoothing and a full understanding of the hyperparatemer. [10] Robert Stanforth, Alhussein Fawzi, Pushmeet Kohli, et al. Calibration and Uncertainty Estimates. robust accuracy using the same number of labels required for achieving high stan-dard accuracy. Supported datasets and NN architectures: ... finding that training models to be invariant to adversarial perturbations requires substantially larger datasets than those required for standard classification. 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. 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. This repository contains code to run Label Smoothing as a means to improve adversarial robustness for deep leatning, supervised classification tasks. 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 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. Key Takeaways. 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