# tradeoffs between robustness and accuracy

Trade-offs between cost and accuracy in active case finding for tuberculosis: A dynamic modelling analysis. “Just like a watch that comes with a water resistance number, we wanted to provide an effective … We study this tradeoff in two settings, adversarial examples and minority groups, creating simple examples which highlight generalization issues as a major source of this tradeoff. Previous works attempt to explain the tradeoff between standard error and robust error in two settings: when no accurate classifier is consistent with the perturbed data (Tsipras et al., 2019; Zhang et al., 2019; Fawzi et al., 2018), and when the hypothesis class is not expressive enough to contain the true classifier (Nakkiran, 2019). In practice, one may prefer to trade-off between robustness and accuracy by introducing weights in (1) to bias more towards the natural error or the boundary error. Taking adversarial training for example, RobustBench: json stats: various plots based on the jsons from model_info (robustness over venues, robustness vs accuracy, etc). Practically, most defense methods determine their accuracy-robustness trade-off by some pre-chosen hyper-parameter. To demystify the trade-offs between robustness and accuracy, in this paper we thoroughly benchmark 18 ImageNet models using multiple robustness metrics, including the distortion, success rate and transferability of adversarial examples between 306 pairs of models. We study this tradeoff in two settings, adversarial examples and minority groups, creating simple examples which highlight generalization issues as a major source of this tradeoff. Here, we investigate the interplay between the robustness of patterning to the changes in receptor synthesis and morphogen synthesis and to the effects of cell-to-cell variability. While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). Theoretically Principled Trade-off between Robustness and Accuracy ( )= p ( ) ⇤⇤ . It has been claimed that trade-offs exist between robustness, fragility, performance, and resource demands in biological and engineering systems (Csete and Doyle, 2002; Kitano, 2004, 2007). The appeal of our approach lies in its simplicity as its devoid of any (costly) hyperparameter optimization sub-steps. We study this tradeoff in two settings, adversarial examples and minority groups, creating simple examples which highlight generalization issues as a major source of this tradeoff. ⇤. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. (NeurIPS 2020) Once-for-All Adversarial Training, In-Situ Trade off between Robustness and Accuracy for Free Posted on 2020-12-04 Edited on 2020-09-30 In NeurIPS'20. Browse our catalogue of tasks and access state-of-the-art solutions. .. H. Wang*, T. Chen*, S. Gui, T. Hu, J. Liu and Z. Wang, Neural Information Processing Systems 2020. hyperparameters to achieve different tradeoffs between ro-bustness and accuracy. 26 Prospect Ave While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). However, adversarial … For minority groups, we show that overparametrization of models can also hurt accuracy. been used to analyze trade-offs between standard and adversarial accuracy [41], and the sample-complexity of adversarial generalization [30]. Moreover, the training process is heavy and hence it becomes impractical to thoroughly explore the trade-off between accuracy and robustness. 08540 Adversarial training augments the training set with perturbations to improve the robust error (over worst-case perturbations), but it often leads to an increase in the standard error (on unperturbed test inputs). https://www.oneworldml.org/upcoming-events, Center for Statistics and Machine Learning Princeton, New Jersey Switching from natural samples to adversarial samples can be … Although the problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Tradeoffs between Robustness and Accuracy While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). Noting that both the natural error and the boundary error involve 0-1 loss functions, our goal is to devise tight differ- entiable upper bounds on both of these terms. Help our scientists and scholars continue their field-shaping work. Here we address several of these key questions. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. Theoretically Principled Trade-off between Robustness and Accuracy Zhang, Hongyang; Yu, Yaodong; Jiao, Jiantao; Xing, Eric P.; El Ghaoui, Laurent; Jordan, Michael I. Abstract. We deﬁne Mutually Exclusive Perturbations (MEPs) as pairs of perturbation types for which robustness to one type implies vulnerability to the other. These results suggest that the "more data" and "bigger models" strategy that works well for the standard setting where train and test distributions are close, need not work on out-of-domain settings. Although this problem has been widely studied empirically, much … To demystify the trade-offs between robustness and accuracy, in this paper we thoroughly benchmark 18 ImageNet models using multiple robustness metrics, including the distortion, success rate and transferability of adversarial examples between 306 pairs of models. Neural network The neural network model is learnt as described in §4.1 — we vary λ (regularization parameter) to learn various models that have different tradeoffs between accuracy and pixels (which translates to power). Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free. Tradeoffs between Robustness and Accuracy, Workshop on New Directions in Optimization Statistics and Machine Learning. Begin typing to search for a section of this site. In this work, we decompose the prediction error for adversarial examples (robust error) as the sum of … We are very interested in collecting new insights about benefits and tradeoffs between different perturbation types. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. 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. This gives us a pareto optimal set of solutions i.e. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. We study this tradeoff in two settings, adversarial examples and minority groups, creating simple examples which highlight generalization issues as a major source of this tradeoff. While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). For adversarial examples, we show that even augmenting with correct data can produce worse models, but we develop a simple method, robust self training, that mitigates this tradeoff using unlabeled data. In NeurIPS 2020. Abstract: We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Haotao Wang*, Tianlong Chen*, Shupeng Gui, Ting-Kuei Hu, Ji Liu, Zhangyang Wang. 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. inherent accuracy-robustness trade-off is established both theoretically, and observed experimentally, by many works [5, 9, 10, 11, 12, 13]. These results suggest that the "more data" and "bigger models" strategy that works well for improving standard accuracy need not work on out-of-domain settings, even in favorable conditions. Determination of the conditions in which this conjecture would hold is of great interest for systems theory in biology. Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribution deviates from the training distribution. 1 Einstein Drive Overview. This is based on joint work with Sang Michael Xie, Shiori Sagawa, Pang Wei Koh, Fanny Yang, John Duchi and Percy Liang. For minority groups, we show that overparametrization of models can hurt accuracy on the minority groups, though it improves standard accuracy. In this work, we quantify the trade-off in terms of the gap between the risk for adversarial examples and the risk for non … [Co-first Author] Once-for-All Adversarial Training: In-Situ Trade off between Robustness and Accuracy for Free . USA. To demystify the trade-offs between robustness and accuracy, in this paper we thoroughly benchmark 18 ImageNet models using multiple robustness metrics, including the distortion, success rate and transferability of adversarial examples between 306 pairs of models. Feel free to suggest a new notebook based on the Model Zoo or the jsons from model_info. Experimental results show that OAT/OATS achieve similar or even superior performance, when compared to dedicatedly trained models. For adversarial examples, we show that even augmenting with correctly annotated data to promote … We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Such transformations may consist of small rotations, hor-izontal ﬂips, brightness or contrast changes (Krizhevsky et al.,2012;Yaeger et al.,1996), or small ‘ pperturbations in vision (Szegedy et al.,2014;Goodfellow et al.,2015… [50] empirically discovered that an appropriately higher CNN model sparsity led to better robustness, whereas over-sparsiﬁcation (e.g., less than 5% non-zero parameters) could in turn cause more fragility. Codes for reproducing robustness-accuracy analysis in "Is Robustness the Cost of Accuracy? a set of solutions that shows the tradeoff between the two objectives. Conflicting relation between robustness and small-world effect. Despite significant progress in the area, foundational open problems remain. Although the problem has been widely studied empirically, much remains unknown concerning the … )/). No code available yet. Carnegie Mellon University We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. -- A Comprehensive Study on the Robustness of 18 Deep Image Classification Models", ECCV 2018 - IBM/ImageNet-Robustness An extension from OAT to OATS, that enables a joint in-situ trade-off among robustness, accuracy, and the computational budget. For adversarial examples, we show that even augmenting with correctly annotated data to promote robustness can produce less accurate models, but we develop a simple method, robust self-training, that mitigates this tradeoff using unlabeled data. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy. Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribtion deviates from the training distribution. Our proposed framework, Once-for-all … Understanding and Mitigating the Tradeoff Between Robustness and Accuracy Aditi Raghunathan * 1Sang Michael Xie Fanny Yang2 John C. Duchi 1Percy Liang Abstract Adversarial training augments the … Consequently, different compression algorithms might lead to different trade-offs between robustness and accuracy. ⇤ ⇤ ⇤:= ⇤ ⇤ ⇤ ⇤ ⇤ = and ⇣ ⌘ ⇤ +⇠. In both cases, the tradeoff persists even with infinite data. Get the latest machine learning methods with code. This paper asks this new question: how to quickly calibrate a trained model in-situ, to examine the achievable trade-offs between its standard and robust accuracies, without (re-)training it many times? It is well known that machine learning methods can be vulnerable to adversarially-chosen perturbations of their inputs. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Our approaches meanwhile cost only one model and no re-training. Princeton, NJ 08544, One World Seminar Series on the Mathematics of Machine Learning, © 2020 The Trustees of Princeton University, SML 310 — Research Projects in Data Science, Old and New Open Questions in Optimization, Molecular Simulation with Machine Learning, Tutorial Workshop on Machine Learning for Experimental Science, Tradeoffs between Robustness and Accuracy. Copyright Â© 2020 Institute for Advanced Study. 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