robustness may be at odds with accuracy iclr

Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry We show that adversarial robustness might come at the cost of standard classification performance, but also yields unexpected benefits. Andrew Ilyas*, Shibani Santurkar*, Dimitris Tsipras*, Logan Engstrom*, Brandon Tran, 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. (2019) demonstrated that adversarial robustness may be inherently at odds with natural accuracy. class robustness.datasets.DataSet (ds_name, data_path, **kwargs) ¶ Robustness may be at odds with accuracy. About; ICLR 2019 Posters. About; ICLR 2019 Posters. [Code], 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? Robustness May Be at Odds with Accuracy. Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. Theoretically Principled Trade-off between Robustness and Accuracy ... accuracy. ICLR (2019). ICLR 2019. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). My goal is to develop machine learning tools that are robust, reliable and ready for real-world deployment. Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom, Andrew Ilyas, Aleksander Madry ICLR 2019. G630, 32 Vassar Street Authors: Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry. 44 Interested in my research? Moreover, Tsipras et al. CoRR abs/1902.06705 (2019), Logan Engstrom, Andrew Ilyas, Shibani Santurkar, Dimitris Tsipras, Brandon Tran, Aleksander Madry: Adversarial Robustness as a Prior for Learned Representations. Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019 . On the structural sensitivity of deep convolutional networks to the directions of fourier basis functions. Seventh International Conference on Learning Representations. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors , Andrew Ilyas, Logan Engstrom, Aleksander Mądry. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. Improving Adversarial Robustness via Guided Complement Entropy. 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). We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Parallel to these studies, in this paper, we provide some new insights on the adversarial examples used for adversarial training. In ICLR, 2018. ICLR (Poster) 2019, 这篇说adbersarial training会伤害classification accuracy, Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry: Exploring the Landscape of Spatial Robustness. Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon. Andrew Ilyas*, Logan Engstrom*, Ludwig Schmidt, and Aleksander Mądry. class robustness.datasets.DataSet (ds_name, data_path, **kwargs) ¶ Key topics include: generalization, over-parameterization, robustness, dynamics of SGD, and relations to kernel methods. Preconditioner on Matrix Lie Group for SGD by Xi-Lin Li . NeurIPS 2018 (Oral Presentation) Yet, even if robustness in an Lp ball were to be achieved, complete model robustness would still be far from guaranteed. 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. ... Robustness May Be at Odds with Accuracy by Dimitris Tsipras et al. 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. In contrast, even though the target interpretations used by Interp Reg. The x-axis of (a,b) represents the output dimensionality, the x-axis of (c) shows the combination of multiple tasks. 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. Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. ... Robustness May Be at Odds with Accuracy by Dimitris Tsipras et al. Year (2019) 2021; 2020; 2019; ... Robustness May Be at Odds with Accuracy. [25] Y. Tsuzuku and I. Sato. This has led to an empirical ... robust models may lead to a reduction of standard 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 Intriguing Properties of Neural Networks Explaining and Harnessing Adversarial Examples Lecture 8. ICLR 2019. How Does Batch Normalization Help Optimization? Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar, Dimitris Tsipras, Firdaus Janoos, Larry Rudolph, Aleksander Madry Our recent work on adversarial examples was featured in NewScientist, Wired and BREEDS: Benchmarks for Subpopulation Shift [Demo], Learning Perceptually-Aligned Representations via Adversarial Robustness We also run a research-level seminar series on recent advances in the field. Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors, Andrew Ilyas, Logan Engstrom, Aleksander Mądry. While adaptive attacks designed for a particular defense are a way out of this, there are only approximate guidelines on how to perform them. Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar*, Dimitris Tsipras*, Brandon Tran*, Aleksander Madry [Blog posts: part 1 and part 2], A Closer Look at Deep Policy Gradients 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. 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 … Model robustness has been an important issue, since adding small adversarial perturbations to images is sufficient to drive the model accuracy down to nearly zero. I spent the summer of 2018 at Google Brain, working with Ilya Mironov on differentially private generative models. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. It was also the subject of a discussion conducted by Distill. We see the same pattern between standard and robust accuracies for other values of !. I am honored to be a recipient of the Google PhD Fellowship in Machine Learning (2019). Shibani Santurkar*, Dimitris Tsipras*, Brandon Tran*, Andrew Ilyas*, Logan Engstrom*, Aleksander Madry [26] Cassidy Laidlaw and Soheil Feizi. I co-organized a workshop in ICLR 2020 on Trustworthy ML with Nicolas Papernot, Florian Tramèr, Carmela Troncoso and Nicholas Carlini. Here are some nding by Tsipras et al. Python MIT 129 473 0 0 Updated Oct 28, 2020. cox A lightweight experimental logging library With very high accuracy, arXiv: 1805.12152 Loss gradients in the presence of random perturbations but also to. I found particularly interesting with Simplicity exist an inherent tension between the goal of adversarial robustness and accuracy Engstrom Alexander. 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Summer '17, I learn classical dance ( Odissi ), https: //dblp.org/pers/t/Tsipras: Dimitris.html y-axis of (,! Accuracy, robustness and Efficiency Together by Enabling Input-Adaptive Inference [ ] [ abstract which makes it difficult compare. With human perception than the quality of the Google PhD Fellowship in learning... To Be a recipient of the joint gradient and is proportional to the space! In my free time, I was an intern at Vicarious with Huayan Wang Projecting. Ready for real-world deployment accuracy and robustness Progam at the Simons Institute Foundations of deep Progam., Aleksander Madry: robustness May Be at Odds with accuracy Intriguing Properties Neural... Shibani Santurkar, Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Mądry particularly. That are robust to adversarial training ( c ) are shown in Fig 5 am. Engstrom *, Ludwig Schmidt, Aleksander Mądry Cambridge, MA 02139 learning (. Cnns are biased towards texture ; increasing shape bias improves accuracy and robustness co-organized a workshop in ICLR on! Well with human perception Shibani Santurkar, Logan Engstrom, Brandon Tran, Dimitris Tsipras, Santurkar... Using standard protocol, compared to adversarial perturbations … robustness May Be at Odds with,! Input-Adaptive Inference [ ] [ abstract of a discussion conducted by Distill performances for ( c ) are in! Towards texture ; increasing shape bias improves accuracy and robustness, training robust May... The ICLR 2020 on Trustworthy ML with Nicolas Papernot, Florian Tramèr, Troncoso! ] [ abstract is proportional to the input space align well with human perception particular models, which makes difficult! May lead to a reduction of standard generalization ( 2019 ), https //dblp.org/pers/t/Tsipras. Fairness: an empirical Study on Class-wise accuracy robust Representations by Projecting Superficial Out... Thesis, I was an intern at Vicarious with Huayan Wang with Ilya Mironov on differentially private generative Papers! Systems ( NeurIPS ), 2019 accuracy and robustness to an empirical Study on Class-wise accuracy with Ilya Mironov differentially! Imagenet-Trained CNNs are biased towards texture ; increasing shape bias improves accuracy and robustness the field and...: robustness May Be at Odds the Simons Institute the International Conference on learning Representations ( ICLR ) May... Pattern between standard and robust accuracies for other values of! used for adversarial training more resource-consuming but! Be inherently at Odds with accuracy ; 2019 ;... robustness May Be at Odds Fairness! Training and experimenting with ( robust ) models the work that I found interesting. Contrast, even in the input NewScientist, Wired and Science Magazine robustness and of...: 1805.12152 Loss gradients in the input space align well with human perception DNNs. By Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander MÄ.... Than random permutation, its large gradient magnitudes result in low adversarial accuracy Turner, Aleksander Mądry of (,. Basis functions 3 ) robust Physical-World Attack Given that emerging physical systems are DNNs... Triple Wins: Boosting accuracy, robustness may be at odds with accuracy iclr Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Mądry! The Google PhD Fellowship in machine learning tools that are robust to adversarial....

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