Despite remarkable success in practice, modern machine learning models have been found to be susceptible to adversarial attacks that make human-imperceptible perturbations to the data, but result in serious and potentially dangerous prediction errors. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. (2018) shows that adversarially robust generalization requires much more labeled data than standard generalization in certain cases. 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. Neural network robustness has recently been highlighted by the existence of … CiteSeerX - Scientific articles matching the query: Adversarially Robust Generalization Requires More Data. Tsipras et al. The conventional wisdom is that more training data should shrink the generalization gap between adversarially-trained models and standard models. Adversarially Robust Generalization Requires More Data[C]. Adversarially Robust Generalization Requires More Data. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect prediction with high confidence. Adversarially Robust Generalization Just Requires More Unlabeled Data. Adversarially Robust Generalization Requires More Data Ludwig Schmidt MIT Shibani Santurkar MIT Dimitris Tsipras MIT ... advances. Download Citation | Adversarially Robust Generalization Requires More Data | Machine learning models are often susceptible to adversarial perturbations of their inputs. Download Citation | Adversarially Robust Generalization Requires More Data | Machine learning models are often susceptible to adversarial perturbations of their inputs. - "More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models" theoretically and experimentally show that training adversarially robust models requires a higher sample complexity compared to regular generalization. Adversarially Robust Generalization Requires More Data Reviewer 1 The paper considered theoretical results on adversarially robust generalization, which studies the robustness of classifiers in the presence of even small noise. Neural network robustness has recently been highlighted by the existence of adversarial examples. However, we study the training of robust classifiers for both Gaussian and Bernoulli models under $\ell_\infty$ attacks, and we prove that more data may actually increase this gap. CiteSeerX - Scientific articles matching the query: Adversarially Robust Generalization Just Requires More Unlabeled Data. In "Adversarially Robust Generalization Requires More Data", Schmidt et al. Title: Adversarially Robust Generalization Requires More Data. We complement our theoretical anal-ysis with experiments on CIFAR10, CIFAR100, SVHN, and Tiny ImageNet, and show that AVmixup significantly im-proves the robust generalization performance and that it reduces the trade-off between standard accuracy and ad- Highlight: Though robust generalization need more data, we show that just more unlabeled data is … Abstract. Bibliographic details on Adversarially Robust Generalization Requires More Data. Browse our catalogue of tasks and access state-of-the-art solutions. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect prediction with high confidence. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of "standard" learning. In this subsection, we show that for this specific problem, just using more unlabeled data … Figure 3: Test loss vs. the size of the training dataset under the Gaussian and Bernoulli model in the classification problem. Adversarially Robust Generalization Requires More Data Schmidt, Ludwig , Santurkar, Shibani , Tsipras, Dimitris , Talwar, Kunal , Madry, Aleksander Dec … Did you find it interesting or useful? Adversarially Robust Generalization Just Requires More Unlabeled Data Neural network robustness has recently been highlighted by the existence of adversarial examples. This is the repository for the paper Adversarially Robust Generalization Just Requires More Unlabeled Data submitted to NeurIPS 2019 ().Code Files @article{schmidt2018adversarially, Adversarially Robust Generalization Requires More Data. Read this arXiv paper as a responsive web page with clickable citations. Published in arXiv, 2019. Machine learning models are often susceptible to adversarial perturbations of their inputs. ... To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. Inspired by the theoretical findings, we propose a new algorithm called PASS by leveraging unlabeled data during adversarial training. data augmentation approach for improving adversarially robust generalization. Many previous works show that the learned networks do not perform well on perturbed test data, and significantly more labeled data is required to achieve adversarially robust generalization. We show that already in a simple natural data model, the sample complexity of robust learning can be significantly larger than that of "standard" learning. Robust Generalization Main question: Does robust generalization require more data? Figure 3: Test loss vs. the size of the training dataset under the Gaussian and Bernoulli model in the classification problem. Neural network robustness has recently been highlighted by the existence of adversarial examples. We further prove that for a specific Gaussian mixture problem illustrated by \cite{schmidt2018adversarially}, adversarially robust generalization can be almost as easy as the standard generalization in supervised learning if a sufficiently large amount of unlabeled data is provided. While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary). Adversarially Robust Generalization Just Requires More Unlabeled Data Neural network robustness has recently been highlighted by the existence of adversarial examples. The conventional wisdom is that more training data should shrink the generalization gap between adversarially-trained models and standard models. .. Although adversarial training is one of the most effective forms of defense against adversarial examples, unfortunately, a large gap exists between test accuracy and training accuracy in adversarial training. We show that in the transductive and semi-supervised settings, PASS achieves higher robust accuracy and defense success rate on the Cifar-10 task. Mark. More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models Lin Chen∗ Yifei Min† Mingrui Zhang‡ Amin Karbasi§ Abstract Despite remarkable success in practice, modern machine learning models have been found Many previous works show that the learned networks do not perform well on perturbed test data, and significantly more labeled data is required to achieve adversarially robust generalization. Adversarially Robust Generalization Requires More Data. Legal NoticesThis is i2kweb version 5.0.0-SNAPSHOT. 04/30/2018 ∙ by Ludwig Schmidt, et al. show that training adversarially robust models increases sample complexity. As the stability part does not depend on any label information, we can optimize this part using unlabeled data. L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, ... we study adversarially robust learning from the viewpoint of generalization. Full Text. Tsipras et al. Part of: ... we study adversarially robust learning from the viewpoint of generalization. Published in arXiv, 2019. ... we study adversarially robust learning from the viewpoint of generalization. (Preprint) Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai*, Tianle Cai*, Di He, Chen Dan, Kun He, John Hopcroft, Liwei Wang. Title: Adversarially Robust Generalization Requires More Data. Adversarially Robust Generalization Requires More Data, Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry. Mark. Authors: Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry. In "Adversarially Robust Generalization Requires More Data", Schmidt et al. show that training adversarially robust models increases sample complexity. Logged in from LodzSite Feedback. Adversarially Robust Generalization Requires More Data. (2019) presents an inherent trade-off between accuracy and robust accuracy and argues that the phenomenon comes from the fact that robust classifiers learn different features. Implemented in one code library. Other Comments Comment by Ilyas et al.. We want to thank all the commenters for the discussion and for spending time designing experiments analyzing, replicating, and expanding upon our results. 文章目录概主要内容高斯模型upper boundlower bound伯努利模型upper boundlower boundSchmidt L, Santurkar S, Tsipras D, et al. To this end, we study a second distributional model that highlights how the data ... generalization requires a more nuanced understanding of the data … Many previous works show that the learned networks do not perform well on perturbed test data, and significantly more labeled data is required to achieve adversarially robust generalization. Adversarially Robust Generalization Requires More Data.