- "More Data Can Expand the Generalization Gap Between Adversarially Robust and Standard Models" 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. .. 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. Theoretically, they analyze two very simple families of datasets, e.g., consisting of two Gaussian distributions corresponding to a two-class problem. Figure 3: Test loss vs. the size of the training dataset under the Gaussian and Bernoulli model in the classification problem. Description [1804.11285] Adversarially Robust Generalization Requires More Data adversarially robust generalization needs much more labeled data compared to standard generalization. (Preprint) Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai*, Tianle Cai*, Di He, Chen Dan, Kun He, John Hopcroft, Liwei Wang. We introduced AdvSNR, which characterizes the hardness of Adv-Robust Gaussian Classi cation. data augmentation approach for improving adversarially robust generalization. Figure 3: Test loss vs. the size of the training dataset under the Gaussian and Bernoulli model in the classification problem. Did you find it interesting or useful? Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai , Tianle Cai , Di He , Chen Dan , Kun He , John E. Hopcroft , Liwei Wang 25 Sep 2019 (modified: 24 Dec 2019) ICLR 2020 Conference Blind Submission Readers: Everyone Read this arXiv paper as a responsive web page with clickable citations. Neural network robustness has recently been highlighted by the existence of … Abstract. NeurIPS 2018. The key insight of our results is based on a risk decomposition theorem, in which the expected robust risk is separated into two parts: the stability part which measures the prediction stability in the presence of perturbations, and the accuracy part which evaluates the standard classification accuracy. Adversarially Robust Generalization Requires More Data Schmidt, Ludwig , Santurkar, Shibani , Tsipras, Dimitris , Talwar, Kunal , Madry, Aleksander Dec … 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 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. Mark. 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- Read this arXiv paper as a responsive web page with clickable citations. Adversarially Robust Generalization Requires More Data[C]. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. Title: Adversarially Robust Generalization Requires More Data. Implemented in one code library. 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. 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. Published in arXiv, 2019. In "Adversarially Robust Generalization Requires More Data", Schmidt et al. Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai , Tianle Cai , Di He , Chen Dan , Kun He , John E. Hopcroft , Liwei Wang 25 Sep 2019 (modified: 24 Dec 2019) ICLR 2020 Conference Blind Submission Readers: Everyone 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. Spotlight presentation. Adversarially Robust Generalization Just Requires More Unlabeled Data Neural network robustness has recently been highlighted by the existence of adversarial examples. Inspired by the theoretical findings, we propose a new algorithm called PASS by leveraging unlabeled data during adversarial training. 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. 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. In "Adversarially Robust Generalization Requires More Data", Schmidt et al. We show that in the transductive and semi-supervised settings, PASS achieves higher robust accuracy and defense success rate on the Cifar-10 task. Download Citation | Adversarially Robust Generalization Requires More Data | Machine learning models are often susceptible to adversarial perturbations of their inputs. Ludwig Schmidt [0] Shibani Santurkar [0] Dimitris Tsipras. Adversarially Robust Generalization Just Requires More Unlabeled Data Neural network robustness has recently been highlighted by the existence of adversarial examples. Logged in from LodzSite Feedback. 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. You can learn more in the main discussion article . To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. Adversarially Robust Generalization Requires More Data. Adversarially Robust Generalization Requires More Data. All Responses Comment by Ilyas et al.. We demonstrate that there exist adversarial examples which are just “bugs”: aberrations in the classifier that are not intrinsic properties of the data distribution. Schmidt et al. CiteSeerX - Scientific articles matching the query: Adversarially Robust Generalization Requires More Data. Adversarially Robust Generalization Requires More Data, Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry. We proved matching upper and lower bounds for minimax excess risk, and an e cient, minimax-optimal algorithm. You can learn more in the main discussion article . To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. Adversarially Robust Generalization Just Requires More Unlabeled Data. Schmidt et al. Legal NoticesThis is i2kweb version 5.0.0-SNAPSHOT. 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. 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. paper “Adversarial examples are not bugs, they are features”. .. Ludwig Schmidt [0] Shibani Santurkar [0] Dimitris Tsipras. 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. Tsipras et al. Adversarially Robust Generalization Requires More Data. ` 1 p d Neural network robustness has recently been highlighted by the existence of adversarial examples. Adversarially Robust Generalization Requires More Data. Implemented in one code library. Title: Adversarially Robust Generalization Requires More Data. 04/30/2018 ∙ by Ludwig Schmidt, et al. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity. 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. While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary). Theoretically, they analyze two very simple families of datasets, e.g., consisting of two Gaussian distributions corresponding to a two-class problem. Highlight: Though robust generalization need more data, we show that just more unlabeled data is … Adversarially Robust Generalization Requires More Data. Machine learning models are often susceptible to adversarial perturbations of their inputs. show that training adversarially robust models increases sample complexity. 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. Download Citation | Adversarially Robust Generalization Requires More Data | Machine learning models are often susceptible to adversarial perturbations of their inputs. Adversarial Vertex Mixup: Toward Better Adversarially Robust Generalization Abstract: Adversarial examples cause neural networks to produce incorrect outputs with high confidence. Published in arXiv, 2019. ... To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. Theorem (informal): There is a natural distribution over points in Rd with the following property: Learning an -robust classifier for this distribution requires times more samples than learning a non-robust classifier. 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. Get the latest machine learning methods with code. data augmentation approach for improving adversarially robust generalization. As the stability part does not depend on any label information, we can optimize this part using unlabeled data. paper “Adversarial examples are not bugs, they are features”. ... we study adversarially robust learning from the viewpoint of generalization. Abstract. 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. theoretically and experimentally show that training adversarially robust models requires a higher sample complexity compared to regular generalization. Browse our catalogue of tasks and access state-of-the-art solutions. Let me know your thoughts in the comments below: In this paper, we theoretically and empirically show that with just more unlabeled data, we can learn a model with better adversarially robust generalization. show that training adversarially robust models increases sample complexity. Schmidt et al. Get the latest machine learning methods with code. We postulate that the difficulty of training robust classifiers stems, at least partially, from this inherently larger sample complexity. Neural network robustness has recently been highlighted by the existence of adversarial examples. (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. In this subsection, we show that for this specific problem, just using more unlabeled data … Description [1804.11285] Adversarially Robust Generalization Requires More Data To better understand this phenomenon, we study adversarially robust learning from the viewpoint of 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. Full Text. 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. Neural network robustness has recently been highlighted by the existence of … neural information processing systems, 2018: 5014-5026. 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. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect prediction with high confidence. Neural network robustness has recently been highlighted by the existence of adversarial examples. What is your opinion on this article? Neural network robustness has recently been highlighted by the existence of adversarial examples. (Preprint) Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai*, Tianle Cai*, Di He, Chen Dan, Kun He, John Hopcroft, Liwei Wang. This article is part of a discussion of the Ilyas et al. Authors: Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry ... To better understand this phenomenon, we study adversarially robust learning from the viewpoint of 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. This article is part of a discussion of the Ilyas et al. Part of: ... we study adversarially robust learning from the viewpoint of generalization. Machine learning models are often susceptible to adversarial perturbations of their inputs. Robust Generalization Main question: Does robust generalization require more data? Bibliographic details on Adversarially Robust Generalization Requires More Data. Adversarially Robust Classi cation requires More Data… Part of: ... we study adversarially robust learning from the viewpoint of generalization. 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 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- Adversarially Robust Generalization Requires More Data. Adversarially Robust Generalization Just Requires More Unlabeled Data Neural network robustness has recently been highlighted by the existence of adversarial examples. Logged in as aitopics-guest. (2018) shows that adversarially robust generalization requires much more labeled data than standard generalization in certain cases. Mark. Adversarially Robust Generalization Just Requires More Unlabeled Data. theoretically and experimentally show that training adversarially robust models requires a higher sample complexity compared to regular generalization. Full Text. To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. Adversarially Robust Generalization Requires More Data. ∙ 0 ∙ share . Adversarially Robust Generalization Just Requires More Unlabeled Data Neural network robustness has recently been highlighted by the existence of adversarial examples. Adversarially Robust Generalization Requires More Data. result for Adversarially Robust Classi cation. Adversarially Robust Generalization Requires More Data. @article{schmidt2018adversarially, CiteSeerX - Scientific articles matching the query: Adversarially Robust Generalization Just Requires More Unlabeled Data. Adversarially robust generalization just requires more unlabeled data. 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. Title: Adversarially Robust Generalization Requires More Data. Machine learning models are often susceptible to adversarial perturbations of their inputs. Schmidt 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 … (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. schmidt2018adversarially shows that adversarially robust generalization requires much more labeled data than standard generalization in certain cases. 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. Bibliographic details on 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. Browse our catalogue of tasks and access state-of-the-art solutions. L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, ... we study adversarially robust learning from the viewpoint of generalization. This is the repository for the paper Adversarially Robust Generalization Just Requires More Unlabeled Data submitted to NeurIPS 2019 ().Code Files CiteSeerX - Scientific articles matching the query: Adversarially Robust Generalization Requires More Data. (2018) shows that adversarially robust generalization requires much more labeled data than standard generalization in certain cases. Neural network robustness has recently been highlighted by the existence of adversarial examples. tsipras2018robustness 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. Adversarially Robust Generalization Requires More Data Ludwig Schmidt MIT Shibani Santurkar MIT Dimitris Tsipras MIT ... advances. ... we study adversarially robust learning from the viewpoint of generalization. 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. 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. 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. Highlight: Though robust generalization need more data, we show that just more unlabeled data is … 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. CiteSeerX - Scientific articles matching the query: Adversarially Robust Generalization Just Requires More Unlabeled Data. Even small perturbations can cause state-of-the-art classifiers with high "standard" accuracy to produce an incorrect prediction with high confidence. L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, ... we study adversarially robust learning from the viewpoint of generalization. Authors: Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Mądry. Data… this article is part of:... we study adversarially robust generalization Just adversarially robust generalization requires more data Unlabeled! Mit Shibani Santurkar MIT Dimitris Tsipras MIT... advances to a two-class problem generalization! Data during adversarial training been highlighted by the theoretical findings, we study robust! Pass by leveraging Unlabeled Data part of a discussion of the Ilyas et al learning models are often to! 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