��-�/)��Ѫ�k��A��ۢ��h=gC9LFٛ�wO��[X�=�������=yv��s�c�\��pdv We take a closer look at this phenomenon and first show that real image datasets are actually separated. The most robust method is obtained by using an average of observed years as jump-off rates. %� 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. /A4 << /Type /ExtGState /CA 0.8 /ca 0.8 >> >> 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 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. Statistically, robustness can be be at oddswith accuracy when no assumptions are made on the data distribution  [TSE+19]. 16 0 obj We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. In this paper, we propose a novel training Princeton, New Jersey However, there seems to be an inherent trade-off between optimizing the model for accuracy and robustness. The more years that are averaged, the better the robustness, but accuracy decreases with more years averaged. << /Type /XObject /Subtype /Form /BBox [ 0 0 387.465625 341.525125 ] /Filter /FlateDecode /FormType 1 /Group 47 0 R /Length 2664 model accuracy and robust-ness forming an embarrassing tradeoff – the improvement of one leads to the drop of the other. ) case for a model trained on CIFAR-10 (ResNet), standard accuracy is 99.20% and robust accuracy is 69.10%. 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. We illustrate this result by training different randomized models with Laplace and Gaussian distributions on CIFAR10/CIFAR100. The challenge remains for as we try to improve the accuracy and robustness si-multaneously. /Font << /F1 55 0 R /F2 56 0 R >> /Pattern << >> In particular, we demonstrate the importance of separating standard and adversarial feature statistics, when trying to pack their learning in one model. 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. 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. /M2 60 0 R /M3 61 0 R >> >> >> The best trade-off in terms of complexity, robustness and discrimination accuracy is achieved by the extended GMM approach. 2011) but can be differently affected by the choice of the jump-off rates. AI Tradeoff: Accuracy or Robustness? We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. These experiments highlight the trade-off between accuracy and robustness that depends on the amount of noise one injects in the network. (Left) The underlying distribution P x denoted by sizes of the circles. ∙ 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). /Resources << /ExtGState << /A1 << /Type /ExtGState /CA 0 /ca 1 >> ٙ���#� �pʫ����0j������_����tB��%�Ly�3�*$�IxN��I�)�K ' �n��fҹ�Å����T:5h��ck ��RQB{�깖�!��j����*y f� �t�< both accuracy and robustness. We consider function interpolation via cubic splines. �r�dA��4G�W�٘$f���G&9mm��B&,٦�r��ڜ��}�c�ʬ)���Z�4�a�^j�0٤��s�׃E�{�E�0�Cf҉ �0�$ir��)�Z0�xz����%Rp�no3C��ţB�2j��j%�N��f�G�28�!b�a/zN6F����RoS������'�Ħb��g�{���|����!�:9���8O�S�On��P���]���7��&k�����Ck�X���.�jL�U�����=����$Gs4{O�T���I����!�")���NPEްn�k�:�%)�~@d�q�J�7$�E��͖@&o��A��W�����r�On��s��Ă]Ns�9Ҡv�C"�_���hx�#�A��_���r���z��RJ,�S�����j�Np��"�C��;�z�@,‰ ��H��1q�|�ft0�78���j�< 6��%��f;� �yd��R9�X/$������i��PI-#�zeP=P��xԨ�#���N�y*�{�� ~����i�GP�>G?�'���N�]V������.`3}¿F�% /PTEX.InfoDict 54 0 R /PTEX.PageNumber 1 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. Furthermore, we show that while the pseudo-2D HMM approach has the best overall accuracy, classification time on current hardware makes it impractical. 08540 In this paper we capture the Robustness-Performance (RP) tradeoff explicitly. 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. /A3 << /Type /ExtGState /CA 1 /ca 1 >> Understanding and Mitigating the Tradeoff Between Robustness and Accuracy 02/25/2020 ∙ by Aditi Raghunathan, et al. 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. ��F��Ù�(�ʟ��aP�����C��-ud�0�����W� �໛*yp�C8��N��Gs ��sCjhu�< Abstract: Deploying machine learning systems in the real world requires both high accuracy … ���#uyw�7�v�=�L��Xcri+�N������Ր�ی����������]�7�R���"ԡ=$3������R��m֐���Z��A��6}�-�� ��0����v��{w�h�m��0�y��٭��}*�>B���tX�X�7e����~���޾89�|�H���w| ��ɟ^9�9���?ﮮ�]. One of the most important questions is how to trade off adversarial robustness against natural accuracy. Abstract: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 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. 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 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. Theoretically Principled Trade-off between Robustness and Accuracy Hongyang Zhang Carnegie Mellon University. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. 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. In this work, we decompose the The best trade-off in terms of complexity, robustness and discrimination accuracy is achieved by the extended GMM approach. 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 Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy. accuracy under attack for this kind of noise (cf Theorem 3). The true function is a staircase. Both accuracy and robustness are important for a mortality fore- cast (Cairns et al. 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. 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 ]. → Within any one model, you can also decide to emphasize either precision or recall. Tradeoffs between Robustness and Accuracy, Workshop on New Directions in Optimization Statistics and Machine Learning. We take a closer look at this phenomenon and first show that real image datasets are actually separated. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. .. T�t�< Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. However, most existing ap-proaches are in a dilemma, i.e. We see the same pattern between standard and robust accuracies for other values of !. We propose a method and define quantities to characterize the trade-off between accuracy and robustness for a given architecture, and provide theoretical insight into the trade-off. We see a clear trade-off between robustness and accuracy. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. %PDF-1.5 We take a closer look at this phenomenon and first show that real image datasets are actually separated. Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribution deviates from the training distribution. Furthermore, we show that while the pseudo-1D 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. /XObject << /DejaVuSans-Oblique-epsilon 57 0 R /M0 58 0 R /M1 59 0 R For minority groups, we show that overparametrization of models can also hurt accuracy. How To Sleep When Wheezing, Suddenly Allergic To Hair Dye, Growing Strelitzia From Seed, King Of Thieves Netflix, Microsoft Azure Essentials Pdf, Reddit Saas Marketing, Bbc Gardeners World, Difference Between Data Analyst And Reporting Analyst, "/>

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We provide a general framework for characterizing the trade-off between accuracy and robustness in supervised learning. 3) Robust Physical-World Attack While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). The team’s benchmark on 18 ImageNet models “revealed a tradeoff in accuracy and robustness,” Chen told EE Times. D�;ݐG/ ��/U ��uB V����p?���W׷���z��zu�Zݽ��mu}'�W�~��f (Left) The underlying distribution P x denoted by sizes of the circles. /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] /Shading << >> Moreover, the training process is heavy and hence it becomes impractical to thoroughly explore the trade-off between accuracy and robustness. Help our scientists and scholars continue their field-shaping work. 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. 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. 2.2 Coming back to original question, Precision-Recall Trade-off or Precision vs Recall? Thus, there always has to be a trade-off between accuracy and robustness. nisms and present a general theoretical analysis of the tradeoff between sensitivity and robustness for decisions based on integrated evidence. /A2 << /Type /ExtGState /CA 0.2 /ca 0.2 >> USA. While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). → 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. This property, in combination with the constancy of h 0, allows us to reason about the tradeoff of speed vs. accuracy under robustness. 1 Einstein Drive Conclusion: Carefully considering the best choice for the jump-off rates is essential when forecasting mortality. stream Adversarial training has been proven to be an effective technique for improving the adversarial robustness of models. /PTEX.FileName (./figs/cifar_tradeoff_truncated.pdf) A key issue, according to IBM Research, is how resistant the AI model is to adversarial attacks. 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. While one can train robust models, this often comes at the expense of standard accuracy (on the training distribution). �fk|�b�J d��L�ɇH%�0E��Ym=��U 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 Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribution deviates from the training distribution. Begin typing to search for a section of this site. 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. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. We consider function interpolation via cubic splines. The metabolic syndrome is a highly complex breakdown of normal physiology characterized by obesity, insulin resistance, hyperlipidemia, and hypertension. 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. Copyright © 2020 Institute for Advanced Study. 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. 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 take a closer look at this phenomenon and first show that real image datasets are actually separated. The most robust method is obtained by using an average of observed years as jump-off rates. %� 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. /A4 << /Type /ExtGState /CA 0.8 /ca 0.8 >> >> 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 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. Statistically, robustness can be be at oddswith accuracy when no assumptions are made on the data distribution  [TSE+19]. 16 0 obj We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. In this paper, we propose a novel training Princeton, New Jersey However, there seems to be an inherent trade-off between optimizing the model for accuracy and robustness. The more years that are averaged, the better the robustness, but accuracy decreases with more years averaged. << /Type /XObject /Subtype /Form /BBox [ 0 0 387.465625 341.525125 ] /Filter /FlateDecode /FormType 1 /Group 47 0 R /Length 2664 model accuracy and robust-ness forming an embarrassing tradeoff – the improvement of one leads to the drop of the other. ) case for a model trained on CIFAR-10 (ResNet), standard accuracy is 99.20% and robust accuracy is 69.10%. 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. We illustrate this result by training different randomized models with Laplace and Gaussian distributions on CIFAR10/CIFAR100. The challenge remains for as we try to improve the accuracy and robustness si-multaneously. /Font << /F1 55 0 R /F2 56 0 R >> /Pattern << >> In particular, we demonstrate the importance of separating standard and adversarial feature statistics, when trying to pack their learning in one model. 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. 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. /M2 60 0 R /M3 61 0 R >> >> >> The best trade-off in terms of complexity, robustness and discrimination accuracy is achieved by the extended GMM approach. 2011) but can be differently affected by the choice of the jump-off rates. AI Tradeoff: Accuracy or Robustness? We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. These experiments highlight the trade-off between accuracy and robustness that depends on the amount of noise one injects in the network. (Left) The underlying distribution P x denoted by sizes of the circles. ∙ 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). /Resources << /ExtGState << /A1 << /Type /ExtGState /CA 0 /ca 1 >> ٙ���#� �pʫ����0j������_����tB��%�Ly�3�*$�IxN��I�)�K ' �n��fҹ�Å����T:5h��ck ��RQB{�깖�!��j����*y f� �t�< both accuracy and robustness. We consider function interpolation via cubic splines. �r�dA��4G�W�٘$f���G&9mm��B&,٦�r��ڜ��}�c�ʬ)���Z�4�a�^j�0٤��s�׃E�{�E�0�Cf҉ �0�$ir��)�Z0�xz����%Rp�no3C��ţB�2j��j%�N��f�G�28�!b�a/zN6F����RoS������'�Ħb��g�{���|����!�:9���8O�S�On��P���]���7��&k�����Ck�X���.�jL�U�����=����$Gs4{O�T���I����!�")���NPEްn�k�:�%)�~@d�q�J�7$�E��͖@&o��A��W�����r�On��s��Ă]Ns�9Ҡv�C"�_���hx�#�A��_���r���z��RJ,�S�����j�Np��"�C��;�z�@,‰ ��H��1q�|�ft0�78���j�< 6��%��f;� �yd��R9�X/$������i��PI-#�zeP=P��xԨ�#���N�y*�{�� ~����i�GP�>G?�'���N�]V������.`3}¿F�% /PTEX.InfoDict 54 0 R /PTEX.PageNumber 1 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. Furthermore, we show that while the pseudo-2D HMM approach has the best overall accuracy, classification time on current hardware makes it impractical. 08540 In this paper we capture the Robustness-Performance (RP) tradeoff explicitly. 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. /A3 << /Type /ExtGState /CA 1 /ca 1 >> Understanding and Mitigating the Tradeoff Between Robustness and Accuracy 02/25/2020 ∙ by Aditi Raghunathan, et al. 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. ��F��Ù�(�ʟ��aP�����C��-ud�0�����W� �໛*yp�C8��N��Gs ��sCjhu�< Abstract: Deploying machine learning systems in the real world requires both high accuracy … ���#uyw�7�v�=�L��Xcri+�N������Ր�ی����������]�7�R���"ԡ=$3������R��m֐���Z��A��6}�-�� ��0����v��{w�h�m��0�y��٭��}*�>B���tX�X�7e����~���޾89�|�H���w| ��ɟ^9�9���?ﮮ�]. One of the most important questions is how to trade off adversarial robustness against natural accuracy. Abstract: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 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. 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 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. Theoretically Principled Trade-off between Robustness and Accuracy Hongyang Zhang Carnegie Mellon University. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. 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. In this work, we decompose the The best trade-off in terms of complexity, robustness and discrimination accuracy is achieved by the extended GMM approach. 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 Adversarial training and its many variants substantially improve deep network robustness, yet at the cost of compromising standard accuracy. accuracy under attack for this kind of noise (cf Theorem 3). The true function is a staircase. Both accuracy and robustness are important for a mortality fore- cast (Cairns et al. 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. 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 ]. → Within any one model, you can also decide to emphasize either precision or recall. Tradeoffs between Robustness and Accuracy, Workshop on New Directions in Optimization Statistics and Machine Learning. We take a closer look at this phenomenon and first show that real image datasets are actually separated. Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. .. T�t�< Although this problem has been widely studied empirically, much remains unknown concerning the theory underlying this trade-off. However, most existing ap-proaches are in a dilemma, i.e. We see the same pattern between standard and robust accuracies for other values of !. We propose a method and define quantities to characterize the trade-off between accuracy and robustness for a given architecture, and provide theoretical insight into the trade-off. We see a clear trade-off between robustness and accuracy. We identify a trade-off between robustness and accuracy that serves as a guiding principle in the design of defenses against adversarial examples. %PDF-1.5 We take a closer look at this phenomenon and first show that real image datasets are actually separated. Standard machine learning produces models that are highly accurate on average but that degrade dramatically when the test distribution deviates from the training distribution. Furthermore, we show that while the pseudo-1D 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. /XObject << /DejaVuSans-Oblique-epsilon 57 0 R /M0 58 0 R /M1 59 0 R For minority groups, we show that overparametrization of models can also hurt accuracy.

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