/ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] /Shading << >> /PTEX.InfoDict 54 0 R /PTEX.PageNumber 1 One of the most important questions is how to trade off adversarial robustness against natural accuracy. Copyright © 2020 Institute for Advanced Study. While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). Statistically, robustness can be be at oddswith accuracy when no assumptions are made on the data distribution  [TSE+19]. %PDF-1.5 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. /A3 << /Type /ExtGState /CA 1 /ca 1 >> Understanding and Mitigating the Tradeoff Between Robustness and Accuracy 02/25/2020 ∙ by Aditi Raghunathan, et al. Abstract Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy. << /Type /XObject /Subtype /Form /BBox [ 0 0 387.465625 341.525125 ] (Left) The underlying distribution P x denoted by sizes of the circles. Furthermore, we show that while the pseudo-1D HMM approach has the best overall accuracy, classification time on current hardware makes it impractical. %� 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. In this paper, we propose a novel training However, most existing ap-proaches are in a dilemma, i.e. USA. Begin typing to search for a section of this site. 08540 While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). In this work, we decompose the ) case for a model trained on CIFAR-10 (ResNet), standard accuracy is 99.20% and robust accuracy is 69.10%. ∙ 42 ∙ share 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). ٙ���#� �pʫ����0j������_����tB��%�Ly�3�*$�IxN��I�)�K ' �n��fҹ�Å����T:5h��ck ��RQB{�깖�!��j����*y f� �t�< Understanding and Mitigating the Tradeoff Between Robustness and Accuracy 0 2 4 6 t 0 2 4 6 f (t) µ f* 0 2 4 t 0 2 4 f ¸ (t) Std Aug X std X ext 0 2 4 t 0 2 4 f ¸ (t) Std RST X std X ext Figure 2. → Within any one model, you can also decide to emphasize either precision or recall. Type 2 diabetes is a major manifestation of this syndrome, although increased risk for cardiovascular disease (CVD) often precedes the onset of frank clinical diabetes. AI Tradeoff: Accuracy or Robustness? Adversarial training has been proven to be an effective technique for improving the adversarial robustness of models. /A2 << /Type /ExtGState /CA 0.2 /ca 0.2 >> We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. 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. We take a closer look at this phenomenon and first show that real image datasets are actually separated. 2.2 Coming back to original question, Precision-Recall Trade-off or Precision vs Recall? Tradeoffs between Robustness and Accuracy, Workshop on New Directions in Optimization Statistics and Machine Learning. Understanding and Mitigating the Tradeoff Between Robustness and Accuracy 0 2 4 6 t 0 2 4 6 f (t) f* 0 2 4 t 0 2 4 f (t) Std Aug X std X ext 0 2 4 t 0 2 4 f (t) Std RST X std X ext Figure 2. We see the same pattern between standard and robust accuracies for other values of !. /Resources << /ExtGState << /A1 << /Type /ExtGState /CA 0 /ca 1 >> 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. Under symmetrically bounded drift-diffusion, accuracy is determined by θ and h 0 , whereas the mean decision time is determined by θ, h 0 , and E [ Ẑ ]. Alarmed by the vulnerability of AI models, researchers at the MIT-IBM Watson AI Lab, including Chen, last month presented a paper focused on the certification of AI robustness. Thus, there always has to be a trade-off between accuracy and robustness. We consider function interpolation via cubic splines. The team’s benchmark on 18 ImageNet models “revealed a tradeoff in accuracy and robustness,” Chen told EE Times. The metabolic syndrome is a highly complex breakdown of normal physiology characterized by obesity, insulin resistance, hyperlipidemia, and hypertension. �fk|�b�J d��L�ɇH%�0E��Ym=��U Theoretically Principled Trade-off between Robustness and Accuracy Hongyang Zhang Carnegie Mellon University. → We use the harmonic mean instead of a simple average because it punishes extreme values.A classifier with a precision of 1.0 and a recall of 0.0 has a simple average of 0.5 but an F1 score of 0. /Filter /FlateDecode /FormType 1 /Group 47 0 R /Length 2664 1 Einstein Drive Princeton, New Jersey This property, in combination with the constancy of h 0, allows us to reason about the tradeoff of speed vs. accuracy under robustness. nisms and present a general theoretical analysis of the tradeoff between sensitivity and robustness for decisions based on integrated evidence. D�;ݐG/ ��/U ��uB V����p?���W׷���z��zu�Zݽ��mu}'�W�~��f /M2 60 0 R /M3 61 0 R >> >> >> We illustrate this result by training different randomized models with Laplace and Gaussian distributions on CIFAR10/CIFAR100. both accuracy and robustness. Furthermore, we show that while the pseudo-2D HMM approach has the best overall accuracy, classification time on current hardware makes it impractical. 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. We want to show that there is a natural trade-o between accuracy and robustness You can be absolutely robust but useless, or absolutely accurate but very vulnerable Intuitively, the existence of trade-o makes sense: You can be very robust, e.g., always claims class 1 regardless what you see. 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. Keywords: Data Augmentation, Out-of-distribution, Robustness, Generalization, Computer Vision, Corruption TL;DR: Simple augmentation method overcomes robustness/accuracy trade-off observed in literature and opens questions about the effect of training distribution on out-of-distribution generalization. The most robust method is obtained by using an average of observed years as jump-off rates. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. Abstract: Deploying machine learning systems in the real world requires both high accuracy … A key issue, according to IBM Research, is how resistant the AI model is to adversarial attacks. By Junko Yoshida 01.30.2019 1 TOKYO — Anyone poised to choose an AI model solely based on its accuracy might want to think again. /Font << /F1 55 0 R /F2 56 0 R >> /Pattern << >> Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribution deviates from the training distribution. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. The best trade-off in terms of complexity, robustness and discrimination accuracy is achieved by the extended GMM approach. Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribution deviates from the training distribution. /PTEX.FileName (./figs/cifar_tradeoff_truncated.pdf) The challenge remains for as we try to improve the accuracy and robustness si-multaneously. 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. accuracy under attack for this kind of noise (cf Theorem 3). These experiments highlight the trade-off between accuracy and robustness that depends on the amount of noise one injects in the network. In particular, we demonstrate the importance of separating standard and adversarial feature statistics, when trying to pack their learning in one model. We provide a general framework for characterizing the trade-off between accuracy and robustness in supervised learning. 2011) but can be differently affected by the choice of the jump-off rates. T�t�< ��F��Ù�(�ʟ��aP�����C��-ud�0�����W� �໛*yp�C8��N��Gs ��sCjhu�< 3) Robust Physical-World Attack Both accuracy and robustness are important for a mortality fore- cast (Cairns et al. 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. The true function is a staircase. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Original Pdf: pdf; Keywords: Adversarial training, Improving generalization, robustness-accuracy tradeoff; TL;DR: Instance adaptive adversarial training for improving robustness-accuracy tradeoff; Abstract: Adversarial training is by far the most successful strategy for improving robustness of neural networks to adversarial attacks. We use the worst-case behavior of a solution as the function representing its robustness, and formulate the decision problem as an optimization of both the robustness criterion and the … Abstract:We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. We see a clear trade-off between robustness and accuracy. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. We find that the tradeoff is favorable: decision speed and accuracy are lost when the integrator circuit is mistuned, but this loss is partially recovered by making the network dynamics robust. x��ZKoT�ޟ_ћHf�vWw�k���%���D�u�w��.vH�~��33��3��\P���0S�����W�!�~"�V��U�T���׊���N�n&��������j�:&��'|\�����iz3��c�;�]L���'�6Y�h;�W-�9�n�j]�#��>��-�/)��Ѫ�k��A��ۢ��h=gC9LFٛ�wO��[X�=�������=yv��s�c�\��pdv We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. 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. Conclusion: Carefully considering the best choice for the jump-off rates is essential when forecasting mortality. While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). /A4 << /Type /ExtGState /CA 0.8 /ca 0.8 >> >> Then you are ultimately robust but not accurate. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. Therefore, we recommend looking into developing a choice for the jump-off rates that is both accurate and robust. stream The choice for the jump-off rates being more a practical problem than a theoretical one is also highlighted by the fact that there are only four papers about the choice for Help our scientists and scholars continue their field-shaping work. Moreover, the training process is heavy and hence it becomes impractical to thoroughly explore the trade-off between accuracy and robustness. .. The best trade-off in terms of complexity, robustness and discrimination accuracy is achieved by the extended GMM approach. However, there seems to be an inherent trade-off between optimizing the model for accuracy and robustness. model accuracy and robust-ness forming an embarrassing tradeoff – the improvement of one leads to the drop of the other. In this paper we capture the Robustness-Performance (RP) tradeoff explicitly. We take a closer look at this phenomenon and first show that real image datasets are actually separated. 16 0 obj The more years that are averaged, the better the robustness, but accuracy decreases with more years averaged. In contrast, the trade-off between robustness and performance is more tractable, and several experimental and computational reports discussing such a trade-off have been published (Ibarra et al, 2002; Stelling et al, 2002; Fischer and Sauer, 2005; Andersson, 2006).In short, the trade-off dictates that high-performance systems are often more fragile than systems with suboptimal performance. 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