Tsipras D, Santurkar S, Engstrom L, Turner A, Madry A (2019) Robustness may be at odds with accuracy. Robustness May Be at Odds with Accuracy We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. In: International conference on learning representations. 13/29 c Stanley Chan 2020. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors , Andrew Ilyas, Logan Engstrom, Aleksander Mądry. ICLR 2019 • Dimitris Tsipras • Shibani Santurkar • Logan Engstrom • Alexander Turner • ... We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. predictions is always almost the same as robust accuracy, indicating that drops in robust accuracy is due to adversarial vulnerability. 438 * 2018: Adversarial examples are not bugs, they are features. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri- bution (Tsipras et al., 2019). We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. moosavi.sm@gmail.com smoosavi.me. Further, we argue that this phenomenon is a consequence of robust classifiers learning fundamentally different feature representations than standard classifiers. ICLR (Poster) 2019. accuracy. ∙ 0 ∙ share . EI. An Unexplained Phenomenon Models trained to be more robust to adversarial attacks seem to exhibit ’interpretable’ saliency maps1 Original Image Saliency map of a robusti ed ResNet50 This phenomenon has a remarkably simple explanation! %��������� Robustness May Be at Odds with Accuracy. Tsipras et al. 1Tsipras et al, 2019: ’Robustness may be at odds with accuracy.’ 3 Robust Training of Graph Convolutional Networks via ... attains improved robustness and accuracy by respecting the latent manifold of ... Tsipras et al. A Ilyas, S Santurkar, D Tsipras, L Engstrom, B Tran, A Madry. These differences, in particular, seem to result in unexpected benefits: the representations learned by robust models tend to align better with salient data characteristics and human perception. Moreover, $\textit{there is a quantitative trade-off between robustness and standard accuracy among simple classifiers. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Mądry ICLR 2019 How Does Batch Normalization Help Optimization? 43 ETHZ Zürich, Switzerland Google Zürich. Mark. These findings also corroborate a similar phenomenon observed empirically in more complex settings. Theorem 2.1(Robustness-accuracy trade-off). D Tsipras, S Santurkar, L Engstrom, A Turner, A Madry. Notice, Smithsonian Terms of Furthermore, recent works Tsipras et al. arXiv preprint arXiv:1805.12152, 2018. Robustness May Be at Odds with Accuracy. found ... With unperturbed data, standard training achieves the highest accuracy and all defense techniques slightly degrade the performance. (2019) showed that robustness may be at odds with accuracy, and a principled trade-off was studied by Zhang et al. Robustness may be at odds with accuracy. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry https://arxiv.org/abs/1805.12152 We show that adversarial robustness often inevitablely results in accuracy loss. Schmidt L, Santurkar S, Tsipras D, Talwar K, ... Chen P, Gao Y (2018) Is robustness the cost of accuracy?—a comprehensive study on the robustness of 18 deep image classification models. In: International conference on learning representations. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al.,2019). Authors:Preetum Nakkiran. Astrophysical Observatory. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al.,2019). Any classifier that attains at least 1dstandard accuracy on D has robust accuracy at mostp 1 pdagainst an ‘¥-bounded adversary with#2h. D Tsipras, S Santurkar, L Engstrom, A Turner, A Madry. 43 ETHZ Zürich, Switzerland Google Zürich. ��& ��RTBҪD_W]2��)>�x�O����hx���/�{gnݟVw��N3? Code for "Robustness May Be at Odds with Accuracy" Jupyter Notebook 13 81 2 1 Updated Nov 13, 2020. mnist_challenge A challenge to explore adversarial robustness of neural networks on MNIST. We show that Parseval networks match the state-of-the-art in terms of accuracy on CIFAR-10/100 and Street View House Numbers (SVHN) while being more robust … 44 Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry: Exploring the Landscape of Spatial Robustness. 1Tsipras et al, 2019: ’Robustness may be at odds with accuracy.’ 3. stream .. Dimitris Tsipras. Agreement NNX16AC86A, Is ADS down? Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019. … Robustness May Be at Odds with Accuracy | Papers With Code Robustness May Be at Odds with Accuracy ICLR 2019 • Dimitris Tsipras • Shibani Santurkar • Logan Engstrom • Alexander Turner • Aleksander Madry We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. We demonstrate that this trade-off between the standard accuracy of a model and its robustness to adversarial perturbations provably exists in a fairly simple and natural setting. l^�&���0sT This bound implies that if p < 1, as standard accuracy approaches 100% (d!0), adversarial accuracy falls to 0%. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. Robustness may be at odds with accuracy. Authors: Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry (Submitted on 30 May 2018 , last revised 9 Sep 2019 (this version, v5)) Abstract: We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. 这篇说adbersarial training会伤害classification accuracy. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. A recent hypothesis [][] even states that both robust and accurate models are impossible, i.e., adversarial robustness and generalization are conflicting goals. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry: Robustness May Be at Odds with Accuracy. .. Deep networks were recently suggested to face the odds between accuracy (on clean natural images) and robustness (on adversarially perturbed images) (Tsipras et al., 2019). D Tsipras; S Santurkar; L Engstrom; A Turner ; A Madry; Adversarial training for free! Robustness may be at odds with accuracy. Along with the extensive applications of CNN models for classification, there has been a growing requirement for their robustness against adversarial examples. Shibani Santurkar [0] Logan Engstrom [0] Alexander Turner. 2 Tehran Polytechnic Iran. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. Figure 2 qualitatively compares SmoothGrad and simple gradients. ... Tsipras D, Santurkar S, Engstrom L, Turner A, Madry A (2019) Robustness may be at odds with accuracy. 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