Get the latest machine learning methods with code. 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. While some recent works propose semi-supervised adversarial learning methods that utilize unlabeled data, they still require class labels. Unlabeled Data Improves Adversarial Robustness We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning. VAT[26]anddeepco-training[30]attempt to utilize adversarial examples in semi-supervised settings, but they require enormous extra unlabeled images. Adversarial training [35] improves robustness by … … Integrating structured biological data by kernel maximum mean discrepancy. [41]的简单高斯模型,该模型显示了标准分类和鲁棒分类… 阅读全文 ; RobustBench: json stats: various plots based on the jsons from model_info (robustness over venues, robustness vs accuracy, etc). Original Pdf: pdf; TL;DR: By differentiating misclassified and correctly classified data, we propose a new misclassification aware defense that improves the state-of-the-art adversarial robustness. It has been theoretically shown that decreasing the input dimensionality of data improves robustness . We demonstrate that for broad classes of distributions and classifiers, there exists a sample complexity … Notebooks. Browse our catalogue of tasks and access state-of-the-art solutions. ICML’19 ... Duchi. Table 7: Experimental results with white box attacks on ResNet18 trained on the CIFAR 10 and CIFAR 100 dataset. that shows a sample complexity gap between standard and robust classification. Bioinformatics, 22(14):e49–e57, 2006. Unlabeled Data Improves Adversarial Robustness, Yair Carmon, Aditi Raghunathan, Ludwig Schmidt, Percy Liang, John C. … This approach improves the state-of-the-art on CIFAR-10 by 4% against the strongest known attack. the training data and the optimization procedure. Unlabeled Data Improves Adversarial Robustness (NIPS 2019): 发现通过引入未标记数据和半监督学习(Self-Training),可以提升模型的鲁棒性。作者单位:Stanford University. Unlabeled data for adversarial robustness. While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary). Adversarial training improves robustness Deep neural networks (DNNs) are vulnerable to adversarial examples crafted by imperceptible perturbations. Hypothesis: 추가적인 unlabeled example들은 adversarial attack에 robustness하게 만들기 위해서 충분하다. Key Takeaways. (section 3, 4.1.2) 관찰1: adversarial robustness는 natural image에 대한 smoothness에 달려있고, 이것은 unlabeled data에서 estimate될 수 있다. Data augmentation by incorporating cheap unlabeled data from multiple domains is a powerful way to improve prediction especially when there is limited labeled data. On standard datasets like CIFAR-10, a simple Unsupervised Adversarial Training (UAT) approach using unlabeled data improves robust accuracy by 21.7% over using 4K supervised examples alone, and captures over 95% of the improvement from the same number of labeled examples. Data augmentation by incorporating cheap unlabeled data from multiple domains is a powerful way to improve prediction especially when there is limited labeled data. Unlabeled Data Improves Adversarial RobustnessUnlabeled Data Improves Adversarial Robustness 我们从理论和经验上证明,对抗性鲁棒性可以从半监督学习中显著获益。理论上,我们重述了Schmidt et al. Runtime Masking and Cleansing In this section, we present the runtime masking and cleans-ing (RMC). For instance, more training data – both labeled and unlabeled – improves robustness [43, 52]. Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning Tianlong Chen1, Sijia Liu2, Shiyu Chang2, Yu Cheng3, Lisa Amini2, Zhangyang Wang1 1Texas A&M University, 2MIT-IBM Watson AI Lab, IBM Research 3Microsoft Dynamics 365 AI Research fwiwjp619,atlaswangg@tamu.edu, fsijia.liu,shiyu.chang,lisa.aminig@ibm.com, yu.cheng@microsoft.com Yair is a PhD student at Stanford University in the Electrical Engineering department, advised by John Duchi and Aaron Sidfo Theoretically, we revisit the simple Gaussian model of Schmidt et al. This paper theoretically and empirically shows that guarantee of non-trivial adversarial robustness only requires more unlabeled data. Future work can be explored by combining these methods with our proposed TLA regularization for better adversarial robustness. Unlabeled Data Improves Adversarial Robustness NeurIPS 2019 Yair Carmon*, Aditi Raghunathan*, Ludwig Schmidt , Percy Liang and John Duchi. Existing adversarial learning approaches mostly use class labels to generate adversarial samples that lead to incorrect predictions, which are then used to augment the training of the model for improved robustness. 3. In addition, unlabeled data [39] and model ensemble [37, 25] have been shown to improve the robustness of the model. For instance, more training data { both labeled and unlabeled { improves robustness [43, 52]. that shows a sample complexity gap between standard and robust classification. A range of defense techniques have been proposed to improve DNN robustness … Improving Adversarial Robustness via Unlabeled Out-of-Domain Data. However, gaining robustness from pretraining is left unexplored. NeurIPS’19 Specialized learning algorithms / loss functions / data preprocessing / unlabeled data ... Intrinsic influence of neural network architectures on adversarial robustness? .. We introduce adversarial training into self-supervision, to provide general-purpose robust pre-trained models for the first time. In this paper, we focus on improving adversarial robustness in the low-label regime by leveraging unlabeled data (e.g., when 1%–10% of labels are available) to build robust representations. The paper theoretically proves that under the Gaussian model, more unlabeled data is enough to certify small robust accuracy (1e-3 in the paper) by their robust self-training algorithm. 06/15/2020 ∙ by Zhun Deng, et al. Among them, adversarial training is the most promising one, based on which, a lot of improvements have been developed, such as adding regularizations or leveraging unlabeled data. ICML’19 Yang, Zhang, Katabi, Xu. On standard datasets like CIFAR-10, a simple Unsupervised Adversarial Training (UAT) approach using unlabeled data improves robust accuracy by 21.7% over using 4K supervised examples alone, and captures over 95% of the improvement from the same number of labeled examples. Because robust training methods have higher sample complexity, there has been significant recent attention on how to effectively utilize unlabeled data to train robust models. 作者单位:Stanford University. Previous work has studied this tradeoff between standard and robust accuracy, but only in the setting where no predictor performs well on both objectives in the infinite data limit. Unlabeled Data Improves Adversarial Robustness We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning.

unlabeled data improves adversarial robustness

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