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In this paper we establish rigorous benchmarks for image classifier robustness.
Adaptive histogram equalization and its variations
Stephen M. Pizer, E. Philip Amburn, John D. Austin, Robert Cromartie, Ari Geselowitz, Trey Greer, Bart Ter Haar Romeny, and John B. Zimmerman · 1987
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Ideal spatial adaptation by wavelet shrinkage
David Donoho and Iain Johnstone · 1993
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Efficient cepstral normalization for robust speech recognition
Fu-Hua Liu, Richard M. Stern, Xuedong Huang, and Alex Acero · 1993
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The Aurora experimental framework for the performance evaluation of speech recognition systems under noisy conditions
Hans-Günter Hirsch and David Pearce · 2000
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Antoni Buades and Bartomeu Coll · 2005
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Histogram equalization of speech representation for robust speech recognition
Ángel de la Torre, Antonio Peinado, José Segura, José Pérez-Córdoba, Ma Carmen Benítez, and Antonio Rubio · 2005
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Aurora-5 experimental framework for the performance evaluation of speech recognition in case of a hands-free speech input in noisy environments, 2007
Hans-Günter Hirsch · 2007
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li jia Li, Kai Li, and Li Fei-Fei · 2009
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Generalized distances between rankings, 2010
Ravi Kumar and Sergei Vassilvitskii · 2010
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Histogram-based subband powerwarping and spectral averaging for robust speech recognition under matched and multistyle training, 2012
Mark Harvilla and Richard Stern · 2012
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Applying convolutional neural networks concepts to hybrid nn-hmm model for speech recognition
Ossama Abdel-Hamid, Abdel rahman Mohamed, Hui Jiang, and Gerald Penn · 2013
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An overview of noise-robust automatic speech recognition
Jinyu Li, Li Deng, Yifan Gong, and Reinhold Haeb-Umbach · 2014
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Intriguing properties of neural networks, 2014
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus · 2014
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Sergey Ioffe and Christian Szegedy · 2015
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Measuring neural net robustness with constraints
Osbert Bastani, Yani Ioannou, Leonidas Lampropoulos, Dimitrios Vytiniotis, Aditya Nori, and Antonio Criminisi · 2016
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Defensive distillation is not robust to adversarial examples, 2016
Nicholas Carlini and David Wagner · 2016
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Image style transfer using convolutional neural networks
Leon Gatys, Alexander Ecker, and Matthias Bethge · 2016
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Power-normalized cepstral coefficients (PNCC) for robust speech recognition
Chanwoo Kim and Richard M. Stern · 2016
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Examining the impact of blur on recognition by convolutional networks, 2016
Igor Vasiljevic, Ayan Chakrabarti, and Gregory Shakhnarovich · 2016
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Aggregated residual transformations for deep neural networks
Saining Xie, Ross Girshick, Piotr Dollár, Zhuowen Tu, and Kaiming He · 2016
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Improving the robustness of deep neural networks via stability training, 2016
Stephan Zheng, Yang Song, Thomas Leung, and Ian Goodfellow · 2016
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Adversarial examples are not easily detected: Bypassing ten detection methods, 2017
Distillation as a defense to adversarial perturbations against deep neural networks, 2017
Nicolas Papernot, Patrick McDaniel, Xi Wu, Somesh Jha, and Ananthram Swami · 2017
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Foolbox v0.8.0: A python toolbox to benchmark the robustness of machine learning models, 2017
Jonas Rauber, Wieland Brendel, and Matthias Bethge · 2017
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Certified defenses for data poisoning attacks
Jacob Steinhardt, Pang Wei Koh, and Percy Liang · 2017
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Cure-tsr: Challenging unreal and real environments for traffic sign recognition
Dogancan Temel, Gukyeong Kwon, Mohit Prabhushankar, and Ghassan AlRegib · 2017
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Why do deep convolutional networks generalize so poorly to small image transformations?
Aharon Azulay and Yair Weiss · 2018
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Evaluating and understanding the robustness of adversarial logit pairing
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Nicholas Carlini and David Wagner · 2017
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Ground-truth adversarial examples, 2017
Nicholas Carlini, Guy Katz, Clark Barrett, and David L. Dill · 2017
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Robust physical-world attacks on deep learning models, 2017
Ivan Evtimov, Kevin Eykholt, Earlence Fernandes, Tadayoshi Kohno, Bo Li, Atul Prakash, Amir Rahmati, and Dawn Song · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Ruth Fong and Andrea Vedaldi · 2017
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Comparing deep neural networks against humans: object recognition when the signal gets weaker, 2017
Robert Geirhos, David H. J. Janssen, Heiko H. Schütt, Jonas Rauber, Matthias Bethge, and Felix A. Wichmann · 2017
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On calibration of modern neural networks
Chuan Guo, Geoff Pleiss, Yu Sun, and Kilian Q. Weinberger · 2017
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Google’s cloud vision api is not robust to noise, 2017
Hossein Hosseini, Baicen Xiao, and Radha Poovendran · 2017
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Logan Engstrom, Andrew Ilyas, and Anish Athalye · 2018
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Using trusted data to train deep networks on labels corrupted by severe noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel · 2018
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Multi-scale dense networks for resource efficient image classification
Gao Huang, Danlu Chen, Tianhong Li, Felix Wu, Laurens van der Maaten, and Kilian Q. Weinberger · 2018
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Adversarial logit pairing
Harini Kannan, Alexey Kurakin, and Ian Goodfellow · 2018
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Open category detection with PAC guarantees
Si Liu, Risheek Garrepalli, Thomas Dietterich, Alan Fern, and Dan Hendrycks · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Do cifar-10 classifiers generalize to cifar-10?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
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Adversarially robust generalization requires more data
Ludwig Schmidt, Shibani Santurkar, Dimitris Tsipras, Kunal Talwar, and Aleksander Madry · 2018
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Towards deep learning models resistant to adversarial attacks
Lukas Schott, Jonas Rauber, Matthias Bethge, and Wieland Brendel · 2018
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Traffic signs in the wild: Highlights from the ieee video and image processing cup 2017 student competition
Dogancan Temel and Ghassan AlRegib · 2018
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Cure-or: Challenging unreal and real environments for object recognition
Dogancan Temel, Jinsol Lee, and Ghassan AlRegib · 2018
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Robert Geirhos, Patricia Rubisch, Claudio Michaelis, Matthias Bethge, Felix A Wichmann, and Wieland Brendel · 2019
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
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