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Recent results show that features of adversarially trained networks for classification, in addition to being robust, enable desirable properties such as invertibility.
Theoretically principled trade-off between robustness and accuracy
Zhang, H.; Yu, Y.; Jiao, J.; Xing, E. P.; Ghaoui, L. E.; and Jordan, M. I. 2019 · 1901
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Analyzing and improving representations with the soft nearest neighbor loss
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Where is the information in a deep neural network?
Achille, A.; and Soatto, S. 2019 · 1905
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Adversarially robust transfer learning
Shafahi, A.; Saadatpanah, P.; Zhu, C.; Ghiasi, A.; Studer, C.; Jacobs, D.; and Goldstein, T. 2019 · 1905
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Learning perceptually-aligned representations via adversarial robustness
Engstrom, L.; Ilyas, A.; Santurkar, S.; Tsipras, D.; Tran, B.; and Madry, A. 2019 · 1906
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Emergence of invariance and disentanglement in deep representations
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The information bottleneck method
Tishby, N.; Pereira, F. C.; and Bialek, W. 2000 · 2000
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Visualizing data using t-SNE
Maaten, L. v. d.; and Hinton, G. 2008 · 2008
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Automated flower classification over a large number of classes
Nilsback, M.-E.; and Zisserman, A. 2008 · 2008
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Imagenet: A large-scale hierarchical image database
Deng, J.; Dong, W.; Socher, R.; Li, L.-J.; Li, K.; and Fei-Fei, L. 2009 · 2009
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Cifar-10 and cifar-100 datasets
Krizhevsky, A.; Nair, V.; and Hinton, G. 2009 · 2009
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MNIST handwritten digit database URL http://yann.lecun.com/exdb/mnist/
LeCun, Y.; and Cortes, C. 2010 · 2010
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Novel Dataset for Fine-Grained Image Categorization
Khosla, A.; Jayadevaprakash, N.; Yao, B.; and Fei-Fei, L. 2011 · 2011
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Reading digits in natural images with unsupervised feature learning
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; and Ng, A. Y. 2011 · 2011
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The Caltech-UCSD Birds-200-2011 Dataset
Wah, C.; Branson, S.; Welinder, P.; Perona, P.; and Belongie, S. 2011 · 2011
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Auto-encoding variational bayes
Kingma, D. P.; and Welling, M. 2013 · 2013
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3D Object Representations for Fine-Grained Categorization
Krause, J.; Stark, M.; Deng, J.; and Fei-Fei, L. 2013 · 2013
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Fine-Grained Visual Classification of Aircraft
Maji, S.; Kannala, J.; Rahtu, E.; Blaschko, M.; and Vedaldi, A. 2013 · 2013
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Towards deep learning models resistant to adversarial attacks
Madry, A.; Makelov, A.; Schmidt, L.; Tsipras, D.; and Vladu, A. 2017 · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
Xiao, H.; Rasul, K.; and Vollgraf, R. 2017 · 2017
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Behrmann, J.; Grathwohl, W.; Chen, R. T.; Duvenaud, D.; and Jacobsen, J.-H. 2018 · 2018
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Optimization methods for large-scale machine learning
Bottou, L.; Curtis, F. E.; and Nocedal, J. 2018 · 2018
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Large scale fine-grained categorization and domain-specific transfer learning
Cui, Y.; Song, Y.; Sun, C.; Howard, A.; and Belongie, S. 2018 · 2018
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Goodfellow, I. J.; Shlens, J.; and Szegedy, C. 2014 · 2014
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New insights and perspectives on the natural gradient method
Martens, J. 2014 · 2014
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CNN features off-the-shelf: an astounding baseline for recognition
Sharif Razavian, A.; Azizpour, H.; Sullivan, J.; and Carlsson, S. 2014 · 2014
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Understanding deep image representations by inverting them
Mahendran, A.; and Vedaldi, A. 2015 · 2015
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Understanding neural networks through deep visualization
Yosinski, J.; Clune, J.; Nguyen, A.; Fuchs, T.; and Lipson, H. 2015 · 2015
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Fisher information properties
Zegers, P. 2015 · 2015
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Deep variational information bottleneck
Alemi, A. A.; Fischer, I.; Dillon, J. V.; and Murphy, K. 2016 · 2016
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CINIC-10 is not ImageNet or CIFAR-10
Darlow, L. N.; Crowley, E. J.; Antoniou, A.; and Storkey, A. J. 2018 · 2018
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Deep image prior
Ulyanov, D.; Vedaldi, A.; and Lempitsky, V. 2018 · 2018
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
Virmaux, A.; and Scaman, K. 2018 · 2018
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Efficient and accurate estimation of lipschitz constants for deep neural networks
Fazlyab, M.; Robey, A.; Hassani, H.; Morari, M.; and Pappas, G. 2019 · 2019
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Adversarial examples are not bugs, they are features
Ilyas, A.; Santurkar, S.; Tsipras, D.; Engstrom, L.; Tran, B.; and Madry, A. 2019 · 2019
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Robustness May Be at Odds with Accuracy
Tsipras, D.; Santurkar, S.; Engstrom, L.; Turner, A.; and Madry, A. 2019 · 2019
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Directional adversarial training for cost sensitive deep learning classification applications
Terzi, M.; Susto, G. A.; and Chaudhari, P. 2020 · 2020
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