[2002.10716] Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Robustness May Be at Odds with Accuracy. Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020) Bibtex » Metadata » Paper » Supplemental » Authors. Fanny Yang [...] Percy Liang. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy A. Raghunathan, S. M. Xie, F. Yang, J. Duchi, P. Liang Accepted to International Conference on Machine Learning, ICML 2020 . Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang. We take a closer look at this phenomenon and first show that real image datasets are actually separated. Robust Encodings: A Framework for Combating Adversarial Typos ACL 2020 Erik Jones, Robin Jia*, Aditi Raghunathan*, Percy Liang . As discussed in the interim report of our ExplAIn Project, the trade-off between the explainability and accuracy of AI decisions may often be a false dichotomy.Very simple AI systems can be highly explainable. Robustness in Continual Learning Adversarial Continual Learning. Understanding and Mitigating the Tradeoff between Robustness and Accuracy. Understanding the tradeoff. Title: Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Authors: Aditi Raghunathan , Sang Michael Xie , Fanny Yang , John Duchi , Percy Liang (Submitted on 25 Feb 2020 ( v1 ), last revised 6 Jul 2020 (this version, v2)) Understanding and Mitigating the Tradeoff between Robustness and Accuracy Aditi Raghunathan , Sang Michael Xie , Fanny Yang , John Duchi , Percy Liang , In particular, we demonstrate the importance of separating standard and adversarial feature statistics, when trying to pack their learning in one model. This publication has not been reviewed yet. Transfer Learning with Adversarially Robust Models Do Adversarially Robust ImageNet Models Transfer Better? Understanding and Mitigating the Tradeoff Between Robustness and Accuracy . Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Author: Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang Subject: Proceedings of the International Conference on Machine Learning 2020 Keywords: Machine Learning, ICML Created Date: 20200708012607Z As shown in Fig. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy ICML 2020 Aditi Raghunathan* , Sang Michael Xie*, Fanny Yang , John Duchi and Percy Liang . Tip: you can also follow us on Twitter Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. Adversarially-Trained Deep Nets Transfer Better Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. Understanding and Mitigating the tradeoff between robustness and accuracy. In simple and relatively small decision trees, for example, it is relatively easy to understand how inputs relate to outputs. As above, a correct decision occurs on trials where E N R ̂ > 0. “Understanding and mitigating the tradeoff between robustness and accuracy“, Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John C. Duchi, Percy Liang, Thirty-seventh International Conference on Machine Learning (ICML), Virtual Conference, July 12-18, 2020. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy 02/25/2020 ∙ by Aditi Raghunathan ∙ 42 Choosing the Sample with Lowest Loss makes SGD Robust Feb 2020; Aditi Raghunathan . Authors: Aditi Raghunathan*, Sang Michael Xie *, Fanny Yang, John C. Duchi, Percy Liang Contact: aditir@stanford.edu, xie@cs.stanford.edu Links: Paper | Video Keywords: adversarial examples, adversarial training, robustness, accuracy, tradeoff, robust self-training. In an attempt to explain the tradeoff between robustness and accuracy,Tsiprasetal.(2019);Zhangetal.(2019);Fawzietal. Understanding the Curse of … International Conference on … In particular,Tsipras et al. Better Robustness-Accuracy Trade-off for Stochastic Defenses Xiao Wang1!, Siyue Wang2!, Pin-Yu Chen3, Yanzhi Wang2, Brian Kulis1, Xue Lin2 and Peter Chin1 1Boston University 2Northeastern University 3IBM Research Abstract Despite achieving remarkable success in various domains, recent studies have uncovered the vul-nerability of deep neural networks to adversar-ial perturbations, … Understanding and Mitigating the Tradeoff between Robustness and Accuracy. 2. There has been substantial prior work towards obtaining a better understanding of the robust-ness problem. The team’s benchmark on 18 ImageNet models “revealed a tradeoff in accuracy and robustness.” (Source: IBM Research) Alarmed by the vulnerability of AI models, researchers at the MIT-IBM Watson AI Lab, including Chen, presented this week a new paper focused on the certification of AI robustness. We present a novel once-for-all adverarial training (OAT) framework that addresses a new and important goal: in-situ “free” trade-off between robustness and accuracy at testing time. Current methods for training robust networks lead to a drop in test accuracy, which has led prior works to posit that a robustness-accuracy tradeoff may be inevitable in deep learning. International Conference on Machine Learning (ICML), 2020. between accuracy and robustness. Michael Xie, Understanding and Mitigating the Tradeoff between Robustness and Accuracy (ICML 2020) Eric Wong, Overfitting in adversarially robust deep learning (ICML 2020) 27 July 2020: Francesco Croce, Reliable Evaluation of Adversarial Robustness with an Ensemble of Diverse Parameter-free Attacks (ICML 2020) Pratyush Maini, Adversarial Robustness Against the Union of … Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, Percy Liang. Get the latest machine learning methods with code. You'll get the lates papers with code and state-of-the-art methods. Understanding and mitigating the tradeoff between robustness and accuracy. Remote Sensing, 2020. average user rating 0.0 out of 5.0 based on 0 reviews The dynamics of these integrators embody the tradeoff between robustness and sensitivity that is the focus of our study ... To estimate decision accuracy with robustness R ̂ > 0, we sum N random increments from this distribution forming the cumulative sum E N R ̂. Intriguing properties of Neural Networks 2 Szegedyet al, 2014 •Deep Neural Networks are highly expressive; reason they succeed but also why they produce uninterpretable solutions with counter … Preprint. Paper 1: Taeuk Jang Understanding and Mitigating the Tradeoff between Robustness and Accuracy Paper 2: ... Paper 2: Yiheng Chi, Theoretically Principled Trade-off between Robustness and Accuracy. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, Percy Liang Presented by: Wissam Kontar, AbhiravGholba 1. rating distribution. Aditi Raghunathan*, Sang Michael Xie*, Fanny Yang, John C. Duchi, Percy Liang. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy by Aditi Raghunathan et al. Adversarial Training Can Hurt Generalization Identifying and Understanding Deep Learning … Tip: you can also follow us on Twitter Sang Michael Xie. Abstract. focusing on understanding the difficulty in achieving adversarial robustness from the perspective of data distribution. While this problem is far from being completely understood, perhaps the simplest explanation is that models lack robustness to distributional shift simply because there is no reason for them to be robust [20, 11, 18]. (2019) demonstrated the inevitable tradeoff between robustness and clean accuracy in some particular examples.Schmidt et al. July 24, 2019 Time: 2:30-3:30pm Room: MSEE 239 Paper 1: Hao Li, Neural Ordinary Differential Equations Paper 2: Grant Bowman, A Neural Algorithm of Artistic Style. (2018);Nakkiran(2019)providesimple constructions that showcase an inherent tension between these objectives even in the limit of infinite … Browse our catalogue of tasks and access state-of-the-art solutions. Weakly supervised deep learning for segmentation of remote sensing imagery. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy (ICML 2020) [robustness-tradeoff-paper] Self-Training for Gradual Domain Adaptation (ICML 2020) [gradual-domain-adaptation] DrRepair: Graph-based, Self-Supervised Program Repair from Diagnostic Feedback (ICML 2020) [michiyasunaga-DrRepair-release] Robustness to Spurious Correlations via Human Annotations … Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. Has understanding and mitigating the tradeoff between robustness and accuracy substantial prior work towards obtaining a Better understanding and Mitigating the tradeoff between robustness and,! Rating 0.0 out of 5.0 based on 0 reviews You 'll get the papers! Rating 0.0 out of 5.0 based on 0 reviews You 'll get the lates papers with code state-of-the-art! … There has been substantial prior work towards obtaining a Better understanding of the robust-ness problem in one model,. 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