Adversarially Robust Generalization Just Requires More Unlabeled Data. Mark. Download Citation | Adversarially Robust Generalization Requires More Data | Machine learning models are often susceptible to adversarial perturbations of their inputs. 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 is the repository for the paper Adversarially Robust Generalization Just Requires More Unlabeled Data submitted to NeurIPS 2019 ().Code Files Machine learning models are often susceptible to adversarial perturbations of their inputs. 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 Schmidt, Ludwig , Santurkar, Shibani , Tsipras, Dimitris , Talwar, Kunal , Madry, Aleksander Dec … This article is part of a discussion of the Ilyas et al. show that training adversarially robust models increases sample complexity. 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. 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. 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. Legal NoticesThis is i2kweb version 5.0.0-SNAPSHOT. 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. Highlight: Though robust generalization need more data, we show that just more unlabeled data is … Theoretically, they analyze two very simple families of datasets, e.g., consisting of two Gaussian distributions corresponding to a two-class problem. Bibliographic details on Adversarially Robust Generalization Requires More Data. Adversarially robust generalization just requires more unlabeled data. Schmidt et al. Adversarially Robust Generalization Requires More Data. What is your opinion on this article? Ludwig Schmidt [0] Shibani Santurkar [0] Dimitris Tsipras. Neural network robustness has recently been highlighted by the existence of adversarial examples. To better understand this phenomenon, 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. The conventional wisdom is that more training data should shrink the generalization gap between adversarially-trained models and standard models. CiteSeerX - Scientific articles matching the query: Adversarially Robust Generalization Requires More 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. Logged in as aitopics-guest. .. 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. L. Schmidt, S. Santurkar, D. Tsipras, K. Talwar, ... we study adversarially robust learning from the viewpoint of generalization. Abstract. theoretically and experimentally show that training adversarially robust models requires a higher sample complexity compared to regular generalization. Schmidt et al. ... we study adversarially robust learning from the viewpoint of generalization. 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). Neural network robustness has recently been highlighted by the existence of adversarial examples. Neural network robustness has recently been highlighted by the existence of … Adversarially Robust Generalization Requires More Data. The conventional wisdom is that more training data should shrink the generalization gap between adversarially-trained models and standard models. Part of: ... we study adversarially robust learning from the viewpoint of generalization. neural information processing systems, 2018: 5014-5026. Adversarially Robust Generalization Requires More Data. Did you find it interesting or useful? 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. Mark. Browse our catalogue of tasks and access state-of-the-art solutions. 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. (Preprint) Adversarially Robust Generalization Just Requires More Unlabeled Data Runtian Zhai*, Tianle Cai*, Di He, Chen Dan, Kun He, John Hopcroft, Liwei Wang. 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. 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. Get the latest machine learning methods with code. paper “Adversarial examples are not bugs, they are features”. CiteSeerX - Scientific articles matching the query: 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. Adversarially Robust Generalization Requires More 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. ... To better understand this phenomenon, we study adversarially robust learning from the viewpoint of generalization. Highlight: Though robust generalization need more data, we show that just more unlabeled data is … Published in arXiv, 2019. Implemented in one code library. ` 1 p d Neural network robustness has recently been highlighted by the existence of adversarial examples. 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.