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[25] Y. Tsuzuku and I. Sato. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). In ICLR, 2018. 44 Interested in my research? Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, Aleksander Madry Published as a conference paper at ICLR 2019 THE SCIENTIFIC METHOD IN THE SCIENCE OF MA-CHINE LEARNING Jessica Zosa Forde Project Jupyter ... robustness, and interpretability have come to the forefront of discussion. ICLR 2019. Dimitris Tsipras合作的一系列paper感觉都很有insight,至少有趣,尤其是去年的:, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry: Robustness May Be at Odds with Accuracy. Here are some nding by Tsipras et al. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors, Andrew Ilyas, Logan Engstrom, Aleksander Mądry. ... ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. Robustness may be at odds with accuracy. A theory of the learnable. Robustness May Be at Odds with Accuracy Intriguing Properties of Neural Networks Explaining and Harnessing Adversarial Examples Lecture 8. 3D point-cloud recognition with deep neural network (DNN) has received remarkable progress on obtaining both high-accuracy recognition and robustness to random point missing (or dropping). Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry 24/32 Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry: Robustness May Be at Odds with Accuracy. Prior Convictions: Black … About; ICLR 2019 Posters. International Conference on Learning Representations (ICLR), May 2016, Best Paper Award. I co-organized a workshop in ICLR 2020 on Trustworthy ML with Nicolas Papernot, Florian Tramèr, Carmela Troncoso and Nicholas Carlini. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors, Andrew Ilyas, Logan Engstrom, Aleksander Mądry. 3) Robust Physical-World Attack Given that emerging physical systems are using DNNs in safety- (2019) demonstrated that adversarial robustness may be inherently at odds with natural accuracy. Preconditioner on Matrix Lie Group for SGD by Xi-Lin Li . Szegedy et al., ICLR 2014. ICLR 2019. Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. Abstract and Figures 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). [Code], Adversarial Examples Are Not Bugs, They Are Features Specifically, even though training models to be adversarially robust can be beneficial in the regime of limited training data, in general, there can be an inherent trade-off between the standard accuracy and adversarially robust accuracy of a model. moosavi.sm@gmail.com smoosavi.me. (2019); Zhang et al. Parallel to these studies, in this paper, we provide some new insights on the adversarial examples used for adversarial training. •For image, robustness is often at odds with generalization •Generalization: Accuracy on clean data •Robustness: Accuracy on adversarial examples •To boost performance on clean data, we propose to add perturbation in the feature space instead of pixel space Robustness may be at odds with accuracy. NeurIPS 2018 (Spotlight Presentation), A Classification-Based Study of Covariate Shift in GAN Distributions For example, on CIFAR-10 with 250 labeled examples we reach 93.73% accuracy (compared to MixMatch's accuracy of 93.58% with 4,000 examples) and a median accuracy … In Proceedings of the International Conference on Learning Representations (ICLR), 2019. ICLR 2019. Specifically, my research has been focused on two broad themes: developing a precise understanding of the functioning of widely-used deep learning techniques; and avenues to make machine learning methods robust and secure from an adversarial viewpoint. Title:Adversarial Robustness May Be at Odds With Simplicity. N.R. Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. The Best Generative Models Papers from the ICLR 2020 Conference Posted May 7, 2020. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. How Does Batch Normalization Help Optimization? We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. About; ICLR 2019 Posters. 2019. 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. ICLR 2020 (Oral Presentation) NeurIPS 2018 (Oral Presentation) This has led to an empirical ... robust models may lead to a reduction of standard accuracy. Seventh International Conference on Learning Representations. Yet, even if robustness in an Lp ball were to be achieved, complete model robustness would still be far from guaranteed. For example, on ImageNet-C, statistics adaptation improves the top1 accuracy from 40.2% to 49%. Year (2019) 2021; 2020; 2019; ... Robustness May Be at Odds with Accuracy. Robustness may be at odds with accuracy D Tsipras, S Santurkar, L Engstrom, A Turner, A Madry Proceedings of the International Conference on Representation Learning (ICLR … , 2018 Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon. David Budden, Alex Matveev, Shibani Santurkar, Shraman Chaudhari, Nir Shavit Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019 . class robustness.datasets.DataSet (ds_name, data_path, **kwargs) ¶ Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference [] [Abstract. [Code and Data], From ImageNet to Image Classification: Contextualizing Progress on Benchmarks We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. ICLR 2020 NEURAL EXECUTION OF GRAPH ALGORITHMS DEEP GRAPH MATCHING CONSENSUS DIRECTIONAL MESSAGE PASSING FOR MOLECULAR GRAPHS A FAIR COMPARISON OF GRAPH NEURAL NETWORKS FOR GRAPH CLASSIFICATION ... Robustness May Be at Odds with Accuracy How Powerful Are Graph Neural Networks? Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar*, Dimitris Tsipras*, Brandon Tran*, Aleksander Madry How Does Batch Normalization Help Optimization? Xin W ang, Fisher Y u, Zi-Yi Dou, and Joseph E Gonzalez. We can also see that for the XEnt 152x2 and 152 models, the smaller model (152) actually has better mCE and equally good top-1 accuracy, indicating that the wider model may be overfitting, but the 152x2 CEB and cCEB models substantially outperform both of them across the board. ICML 2019: 1802-1811, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry: Adversarial Examples Are Not Bugs, They Are Features. [Blog post], Title:Adversarial Robustness May Be at Odds With Simplicity. The success of deep neural networks is clouded by two issues that largely remain open to this day: the abundance of adversarial attacks that fool neural networks with small perturbations and the lack of interpretation for the predictions they make. Preconditioner on Matrix Lie Group for SGD by Xi-Lin Li . The y-axis of (b) is classification accuracy. Robustness May Be at Odds with Accuracy. Download PDF. Robustness may be at odds with accuracy, arXiv: 1805.12152 Loss gradients in the input space align well with human perception. G630, 32 Vassar Street Learning both Weights and Connections for Efficient Neural Networks Song Han, Jeff Pool, John Tran, William J. Dally Advances in Neural Information Processing Systems (NIPS), December 2015. In ICLR, 2019. ... Robustness may be at odds with accuracy, Tsipras et al., NeurIPS 2018. Year (2019) 2021; 2020; 2019; ... Robustness May Be at Odds with Accuracy. ICLR 2019. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. 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. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). In Summer '17, I was an intern at Vicarious with Huayan Wang. Szegedy et al., ICLR 2014. [Blog posts: part 1 and part 2], A Closer Look at Deep Policy Gradients However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. 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, and Aleksander Madry (ICLR 2019) ICML 2020 For my Master's Thesis, I worked with Bipin Rajendran on artificial neural networks. We see the same pattern between standard and robust accuracies for other values of !. ∙ 0 ∙ share . Moreover, we find that this technique can further improve state-of-the-art robust … In Proceedings of the ICLR. I am a PhD student in Computer Science at MIT, where I am fortunate to be co-advised by Aleksander Madry and Nir Shavit. Adversarial robustness: Robustness May Be at Odds with Accuracy. Functional adversarial attacks. On the structural sensitivity of deep convolutional networks to the directions of fourier basis functions. 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). On the ImageNet classification task, we demonstrate a network with an accuracy-robustness area (ARA) of 0.0053, an ARA 2.4 times greater than the previous state-of-the-art value. BREEDS: Benchmarks for Subpopulation Shift Andrew Ilyas*, Shibani Santurkar*, Dimitris Tsipras*, Logan Engstrom*, Brandon Tran, Aleksander Madry Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry: Robustness May Be at Odds with Accuracy. ^ Robustness May Be at Odds with Accuracy, ICLR 2019 ^ Adversarial Examples Are Not Bugs, They Are Features, NeurIPS 2019 ^ A Fourier Perspective on Model Robustness in … [Blog post], Identifying Statistical Bias in Dataset Replication ICLR (2019). NeurIPS 2019 ICML 2019 ICLR 2019. Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom, Andrew Ilyas, Aleksander Madry ICLR (2019). [27] Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, and Jacob Steinhardt. Robustness may be at odds with accuracy. [Datasets], Robustness May Be at Odds with Accuracy Chaoning Zhang, Philipp Benz, Dawit Mureja Argaw, Seokju Lee, Junsik Kim, Francois Rameau, Jean-Charles Bazin, In So Kweon. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. ICLR 2019, How Does Batch Normalization Help Optimization? Shibani Santurkar*, Dimitris Tsipras*, Andrew Ilyas*, Aleksander Madry NeurIPS 2018 (Oral Presentation) , [Short video] 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. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019. 03/23/2019 ∙ by Hao-Yun Chen, et al. This is a summary of my impressions from this year’s ICLR, which took place in New Orleans from the 6.-9.May and where I presented our work on GANs.The first part of this post covers my general impressions and is entirely based on my personal views and experiences. 43 ETHZ Zürich, Switzerland Google Zürich. In Proceedings of … Robustness may be at odds with accuracy. The robust performances for (c) are shown in Fig 5. In Advances in Neural Information Processing Systems (NeurIPS), 2019. Readings. may be better than random permutation, its large gradient magnitudes result in low adversarial accuracy. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. [Blog post], Moreover, Tsipras et al. Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy. It was also the subject of a discussion conducted by Distill. ICLR | 2019 . The International Conference on Learning Representations (ICLR) ... propose a novel combination of adversarial training and provable defenses which produces a model with state-of-the-art accuracy and certified robustness on CIFAR-10. The second part covers some of the work that I found particularly interesting. ICLR 2019. ICLR 2019. 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 by Dimitris Tsipras et al. Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. Before coming to MIT, I graduated from Indian Institute of Technology Bombay in 2015 with a Dual Degree (Bachelors and Masters) in Electrical Engineering. NeurIPS 2019: 1260-1271, Nicholas Carlini, Anish Athalye, Nicolas Papernot, Wieland Brendel, Jonas Rauber, Dimitris Tsipras, Ian J. Goodfellow, Aleksander Madry, Alexey Kurakin: On Evaluating Adversarial Robustness. The Odds are Odd: A Statistical Test for Detecting Adversarial Examples Using Pre-Training Can Improve Model Robustness and Uncertainty ME-Net: Towards Effective Adversarial Robustness with … moosavi.sm@gmail.com smoosavi.me. ICML 2017, Google PhD Fellowship in Machine Learning (2019). Introducing Dense Shortcuts to ResNet. Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. of standard performance and adversarial robustness might be fundamentally at odds. Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry CIFAR-10 (robustness.datasets.CIFAR) CINIC-10 (robustness.datasets.CINIC) A2B: horse2zebra, summer2winter_yosemite, apple2orange (robustness.datasets.A2B) Using robustness as a general training library (Part 2: Customizing training) shows how to add custom datasets to the library. Stata Center, MIT Zehao Huang and ... Robustness may be at odds with accuracy. Abstract: We show that there may exist an inherent tension between the goal of adversarial … unlabeled data improves adversarial robustness github. We also run a research-level seminar series on recent advances in the field. Shibani Santurkar*, Dimitris Tsipras*, Andrew Ilyas*, Aleksander Madry NeurIPS 2018 (Oral Presentation) , [Short video] The y-axis of (a,c) is the L2 norm of the joint gradient and is proportional to the model’s adversarial vulnerability. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019. Join the seminar mailing list for talk announcements. ICML 2020 ... ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. Moreover, adaptive evaluations are highly customized for particular models, which makes it difficult to compare different defenses. CIFAR-10 (ResNet), standard accuracy is 99.20% and robust accuracy is 69.10%. 这篇说adbersarial training会伤害classification accuracy. Improving Adversarial Robustness via Guided Complement Entropy. On the ImageNet classification task, we demonstrate a network with an accuracy-robustness area (ARA) of 0.0053, an ARA 2.4 times greater than the previous state-of-the-art value. NeurIPS 2019 ICML 2019 ICLR 2019. Seventh International Conference on Learning Representations. In International Conference on Learning Representations (ICLR), 2019. 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