Understanding the Curse of … 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. 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, … 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 ̂. between accuracy and robustness. In simple and relatively small decision trees, for example, it is relatively easy to understand how inputs relate to outputs. Preprint. 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 . Transfer Learning with Adversarially Robust Models Do Adversarially Robust ImageNet Models Transfer Better? 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. 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. Aditi Raghunathan*, Sang Michael Xie*, Fanny Yang, John C. Duchi, Percy Liang. Abstract. average user rating 0.0 out of 5.0 based on 0 reviews 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. Tip: you can also follow us on Twitter As shown in Fig. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy . [2002.10716] Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Adversarial Training Can Hurt Generalization Identifying and Understanding Deep Learning … 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. International Conference on … Robustness May Be at Odds with Accuracy. Understanding and Mitigating the Tradeoff between Robustness and Accuracy Aditi Raghunathan , Sang Michael Xie , Fanny Yang , John Duchi , Percy Liang , 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. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy by Aditi Raghunathan et al. (2018);Nakkiran(2019)providesimple constructions that showcase an inherent tension between these objectives even in the limit of infinite … (2019) demonstrated the inevitable tradeoff between robustness and clean accuracy in some particular examples.Schmidt et al. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. We take a closer look at this phenomenon and first show that real image datasets are actually separated. 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 … In an attempt to explain the tradeoff between robustness and accuracy,Tsiprasetal.(2019);Zhangetal.(2019);Fawzietal. 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. 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)) Robust Encodings: A Framework for Combating Adversarial Typos ACL 2020 Erik Jones, Robin Jia*, Aditi Raghunathan*, Percy Liang . Aditi Raghunathan, Sang Michael Xie, Fanny Yang, John Duchi, Percy Liang. focusing on understanding the difficulty in achieving adversarial robustness from the perspective of data distribution. 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. Understanding and mitigating the tradeoff between robustness and accuracy. Understanding and Mitigating the tradeoff between robustness and accuracy. You'll get the lates papers with code and state-of-the-art methods. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy ICML 2020 Aditi Raghunathan* , Sang Michael Xie*, Fanny Yang , John Duchi and Percy Liang . Understanding and Mitigating the Tradeoff between Robustness and Accuracy. Understanding the tradeoff. 2. This publication has not been reviewed yet. rating distribution. In particular, we demonstrate the importance of separating standard and adversarial feature statistics, when trying to pack their learning in one model. 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 Remote Sensing, 2020. Understanding and Mitigating the Tradeoff between Robustness and Accuracy. 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 … Get the latest machine learning methods with code. 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]. In particular,Tsipras et al. Fanny Yang [...] Percy Liang. “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. Sang Michael Xie. Tip: you can also follow us on Twitter Browse our catalogue of tasks and access state-of-the-art solutions. Feb 2020; Aditi Raghunathan . Robustness in Continual Learning Adversarial Continual Learning. As above, a correct decision occurs on trials where E N R ̂ > 0. There has been substantial prior work towards obtaining a better understanding of the robust-ness problem. 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. Adversarially-Trained Deep Nets Transfer Better Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. 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