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We demonstrate that the Conditional Entropy Bottleneck (CEB) can improve model robustness.
The information bottleneck method
Tishby, N., Pereira, F. C., and Bialek, W · 2000
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
Earlier work this paper cites.
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2015
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Deepfool: a simple and accurate method to fool deep neural networks
Moosavi-Dezfooli, S.-M., Fawzi, A., and Frossard, P · 2016
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Emergence of Invariance and Disentangling in Deep Representations
Achille, A. and Soatto, S · 2017
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Deep Variational Information Bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2017
Earlier work this paper cites.
Adversarial transformation networks: Learning to generate adversarial examples
Baluja, S. and Fischer, I · 2017
Cited alongside, same era.
Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
Cited alongside, same era.
Robust physical-world attacks on deep learning models
Eykholt, K., Evtimov, I., Fernandes, E., Li, B., Rahmati, A., Xiao, C., Prakash, A., Kohno, T., and Song, D · 2017
Cited alongside, same era.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
Cited alongside, same era.
Information dropout: Learning optimal representations through noisy computation
Achille, A. and Soatto, S · 2018
Cited alongside, same era.
A discussion of ’adversarial examples are not bugs, they are features’
Engstrom, L., Gilmer, J., Goh, G., Hendrycks, D., Ilyas, A., Madry, A., Nakano, R., Nakkiran, P., Santurkar, S., Tran, B., Tsipras, D., and Wallace, E · 2019
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Cloud TPU ResNet Implementation
Google TensorFlow Team · 2019
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Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D. and Dietterich, T · 2019
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Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., and Song, D · 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
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Cited alongside, same era.
Autoaugment: Learning augmentation policies from data
Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., and Le, Q. V · 2018
Cited alongside, same era.
The Conditional Entropy Bottleneck
Fischer, I · 2018
Cited alongside, same era.
Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2018
Cited alongside, same era.
Adversarial examples are not easily detected: Bypassing ten detection methods
Carlini, N. and Wagner, D
Cited in the paper.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S
Cited in the paper.
Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S
Cited in the paper.
Improving robustness without sacrificing accuracy with patch gaussian augmentation
Lopes, R. G., Yin, D., Poole, B., Gilmer, J., and Cubuk, E. D · 2019
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Learnability for the information bottleneck
Wu, T., Fischer, I., Chuang, I., and Tegmark, M · 2019
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Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., Maaten, L. v. d., Yuille, A. L., and He, K · 2019
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A fourier perspective on model robustness in computer vision
Yin, D., Lopes, R. G., Shlens, J., Cubuk, E. D., and Gilmer, J · 2019
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