Prior Convictions: Black-Box Adversarial Attacks with Bandits and Priors, Andrew Ilyas, Logan Engstrom, Aleksander Mądry. ICML 2020 Shibani Santurkar*, Dimitris Tsipras*, Brandon Tran*, Andrew Ilyas*, Logan Engstrom*, Aleksander Madry Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. ... ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. Adversarial trained is more \meaningful". NeurIPS 2019 ICML 2019 ICLR 2019. [Code], Adversarial Examples Are Not Bugs, They Are Features ... Robustness May Be at Odds with Accuracy by Dimitris Tsipras et al. ICML 2020 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. Yet, even if robustness in an Lp ball were to be achieved, complete model robustness would still be far from guaranteed. ICLR 2019, How Does Batch Normalization Help Optimization? I spent the summer of 2018 at Google Brain, working with Ilya Mironov on differentially private generative models. Preconditioner on Matrix Lie Group for SGD by Xi-Lin Li . 03/23/2019 ∙ by Hao-Yun Chen, et al. BREEDS: Benchmarks for Subpopulation Shift Logan Engstrom*, Andrew Ilyas*, Shibani Santurkar*, Dimitris Tsipras*, Brandon Tran*, Aleksander Madry The y-axis of (b) is classification accuracy. On the structural sensitivity of deep convolutional networks to the directions of fourier basis functions. Introducing Dense Shortcuts to ResNet. ICLR | 2019 . ^ 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 … Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference [] [Abstract. 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, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. Moreover, Tsipras et al. For example, on ImageNet-C, statistics adaptation improves the top1 accuracy from 40.2% to 49%. [Code and Data], From ImageNet to Image Classification: Contextualizing Progress on Benchmarks In Search of the Real Inductive Bias: On the Role of Implicit Regularization in Deep Learning ; Characterizing Implicit Bias in … [Blog post], About; ICLR 2019 Posters. Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. ResNet ImageNet Code. Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. 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. [Blog post], Identifying Statistical Bias in Dataset Replication Szegedy et al., ICLR 2014. 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 !. How Does Batch Normalization Help Optimization? ICLR 2019. However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. ICLR 2019. It was also the subject of a discussion conducted by Distill. 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. 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. Philipp Benz, Chaoning Zhang, Adil Karjauv, In So Kweon. We show that there exists an inherent tension between the goal of adversarial robustness and that of standard generalization. Szegedy et al., ICLR 2014. International Conference on Learning Representations (ICLR), May 2016, Best Paper Award. 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. Shibani Santurkar*, Dimitris Tsipras*, Andrew Ilyas*, Aleksander Madry NeurIPS 2019: 125-136, 这篇分离出模型使用的robust features和non robust features(一些奇怪但是对分类有效的features)。虽然non robust features导致adbersarial attack,但在测试集上有帮助, Shibani Santurkar, Andrew Ilyas, Dimitris Tsipras, Logan Engstrom, Brandon Tran, Aleksander Madry: Image Synthesis with a Single (Robust) Classifier. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). Evaluation of adversarial robustness is often error-prone leading to overestimation of the true robustness of models. Seventh International Conference on Learning Representations. unlabeled data improves adversarial robustness github. ... Robustness may be at odds with accuracy, Tsipras et al., NeurIPS 2018. Logan Engstrom, Brandon Tran, Dimitris Tsipras, Ludwig Schmidt, Aleksander Madry: Exploring the Landscape of Spatial Robustness. The y-axis of (a,c) is the L2 norm of the joint gradient and is proportional to the model’s adversarial vulnerability. Andrew Ilyas*, Logan Engstrom*, Ludwig Schmidt, and Aleksander Mądry. You train using standard protocol, compared to adversarial training. Learning Robust Representations by Projecting Superficial Statistics Out by Haohan Wang et al. NeurIPS 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 D Tsipras, S Santurkar, L Engstrom, A Turner, A Madry Proceedings of the International Conference on Representation Learning (ICLR … , 2018 During Summer '19, I attended the Foundations of Deep Learning Progam at the Simons Institute. 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. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, and Aleksander Madry. ... ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. Specifically, training robust models may not only be more resource-consuming, but also lead to a reduction of standard accuracy. 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. In Proceedings of the ICLR. •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. ICLR (2019). NeurIPS 2019 (Spotlight Presentation) [Blog post], Image Synthesis with a Single (Robust) Classifier Robustness May Be at Odds with Accuracy Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry ICLR 2019 . Title:Adversarial Robustness May Be at Odds With Simplicity. Shibani Santurkar*, Dimitris Tsipras*, Andrew Ilyas*, Aleksander Madry NeurIPS 2018 (Oral Presentation) , [Short video] 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. Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry: Robustness May Be at Odds with Accuracy. Parallel to these studies, in this paper, we provide some new insights on the adversarial examples used for adversarial training. 3) Robust Physical-World Attack Given that emerging physical systems are using DNNs in safety- 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. Cambridge, MA 02139. ICLR 2019. 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. In ICLR, 2019. [Code], 24/32 Python MIT 129 473 0 0 Updated Oct 28, 2020. cox A lightweight experimental logging library NeurIPS 2018 (Spotlight Presentation), A Classification-Based Study of Covariate Shift in GAN Distributions However, they are able to learn non-robust classifiers with very high accuracy, even in the presence of random perturbations. 43 ETHZ Zürich, Switzerland Google Zürich. NeurIPS 2018 (Oral Presentation) 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? 这篇说adbersarial training会伤害classification accuracy. 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. Download PDF. We recently released our codebase for training and experimenting with (robust) models. I am a PhD student in Computer Science at MIT, where I am fortunate to be co-advised by Aleksander Madry and Nir Shavit. Google Scholar; Leslie G. Valiant. ICLR 2019 • Dimitris Tsipras • Shibani Santurkar • Logan Engstrom • Alexander Turner • Aleksander Madry. Learning Robust Representations by Projecting Superficial Statistics Out by Haohan Wang et al. is how to trade off adversarial robustness against natural accuracy. Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom*, Alexander Turner, Aleksander Madry Prior Convictions: Black … Abstract: We show that there may exist an inherent tension between the goal of adversarial … 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 ICLR, 2018. We show that there may exist an inherent tension between the goal of adversarial robustness and that of standard generalization. Zehao Huang and ... Robustness may be at odds with accuracy. Robustness may be at odds with accuracy. ICLR (2019). Year (2019) 2021; 2020; 2019; ... Robustness May Be at Odds with Accuracy. Robustness may be at odds with accuracy, arXiv: 1805.12152 Loss gradients in the input space align well with human perception. How Does Batch Normalization Help Optimization? [Blog post], [Demo], Learning Perceptually-Aligned Representations via Adversarial Robustness [27] Daniel Kang, Yi Sun, Dan Hendrycks, Tom Brown, and Jacob Steinhardt. ... Robustness May Be at Odds with Accuracy by Dimitris Tsipras et al. The second part covers some of the work that I found particularly interesting. NeurIPS 2019 ICML 2019 ICLR 2019. How Does Batch Normalization Help Optimization? The x-axis of (a,b) represents the output dimensionality, the x-axis of (c) shows the combination of multiple tasks. unlabeled data improves adversarial robustness github. Adversarial robustness: Robustness May Be at Odds with Accuracy. Authors: Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Madry. Before coming to MIT, I graduated from Indian Institute of Technology Bombay in 2015 with a Dual Degree (Bachelors and Masters) in Electrical Engineering. CIFAR-10 (ResNet), standard accuracy is 99.20% and robust accuracy is 69.10%. Authors:Preetum Nakkiran. Stata Center, MIT Title:Adversarial Robustness May Be at Odds With Simplicity. Statistically, robustness can be be at odds with accuracy when no assumptions are made on the data distri-bution (Tsipras et al., 2019). [Blog post], Implementation Matters in Deep RL: A Case Study on PPO and TRPO 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) , … 44 Interested in my research? 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). Dimitris Tsipras*, Shibani Santurkar*, Logan Engstrom, Andrew Ilyas, Aleksander Madry Abstract: Current techniques in machine learning are so far are unable to learn classifiers that are robust to adversarial perturbations. ICLR 2019. of standard performance and adversarial robustness might be fundamentally at odds. CNN Pixel space Feature space We provide a general framework for characterizing the trade-off between accuracy and robustness in supervised learning. I am honored to be a recipient of the Google PhD Fellowship in Machine Learning (2019). ICLR 2019. Robustness May Be at Odds with Accuracy. For adversarial robustness evaluation, we use strong attacks including PGD100 and MIM100 for attacking the segmentation accuracy 2 2 2 Suffixed number indicates number of steps for attack, and use 100 steps Houdini to attack the non-differentiable mIoU of the Segmentation model directly. [Blog post], Look at the gradient of the loss with respect to the input. Robustness May Be at Odds with Accuracy, Dimitris Tsipras, Shibani Santurkar, Logan Engstrom, Alexander Turner, Aleksander Mądry. ICLR 2019. 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