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We attempt to interpret how adversarially trained convolutional neural networks (AT-CNNs) recognize objects.
Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I · 1901
Earlier work this paper cites.
Theoretically principled trade-off between robustness and accuracy
Zhang, H., Yu, Y., Jiao, J., Xing, E. P., Ghaoui, L. E., and Jordan, M. I · 1901
Earlier work this paper cites.
You only propagate once: Painless adversarial training using maximal principle
Zhang, D., Zhang, T., Lu, Y., Zhu, Z., and Dong, B · 1905
Earlier work this paper cites.
You only propagate once: Painless adversarial training using maximal principle
Zhang, D., Zhang, T., Lu, Y., Zhu, Z., and Dong, B · 1905
Earlier work this paper cites.
Caltech-256 object category dataset
Griffin, G., Holub, A., and Perona, P · 2007
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Caltech-256 object category dataset
Griffin, G., Holub, A., and Perona, P · 2007
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Erhan, D., Bengio, Y., Courville, A., and Vincent, P · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
Earlier work this paper cites.
Visualizing higher-layer features of a deep network
Erhan, D., Bengio, Y., Courville, A., and Vincent, P · 2009
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., and Malik, J · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
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A neural algorithm of artistic style
Gatys, L. A., Ecker, A. S., and Bethge, M · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T · 2015
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Shaham, U., Yamada, Y., and Negahban, S · 2015
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
Earlier work this paper cites.
A neural algorithm of artistic style
Gatys, L. A., Ecker, A. S., and Bethge, M · 2015
Earlier work this paper cites.
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T · 2015
Earlier work this paper cites.
Shaham, U., Yamada, Y., and Negahban, S · 2015
Earlier work this paper cites.
On the performance of googlenet and alexnet applied to sketches
Ballester, P. and de Araújo, R. M · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2016
Cited alongside, same era.
On the performance of googlenet and alexnet applied to sketches
Ballester, P. and de Araújo, R. M · 2016
Cited alongside, same era.
Adversarial examples in the physical world
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Later among the works it cites.
Random mask: Towards robust convolutional neural networks
Luo, T., Cai, T., Zhang, M., Chen, S., and Wang, L · 2018
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Later among the works it cites.
Adversarially robust generalization requires more data
Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., and Madry, A · 2018
Later among the works it cites.
Certifiable distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J · 2018
Later among the works it cites.
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Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2016
Cited alongside, same era.
Towards interpretable deep neural networks by leveraging adversarial examples
Dong, Y., Su, H., Zhu, J., and Bao, F · 2017
Cited alongside, same era.
Arbitrary style transfer in real-time with adaptive instance normalization
Huang, X. and Belongie, S · 2017
Cited alongside, same era.
Measuring the tendency of cnns to learn surface statistical regularities
Jo, J. and Bengio, Y · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., and Lerer, A · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Cited alongside, same era.
Constructing unrestricted adversarial examples with generative models
Song, Y., Shu, R., Kushman, N., and Ermon, S · 2018
Later among the works it cites.
Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2018
Later among the works it cites.
Non-local neural networks
Wang, X., Girshick, R., Gupta, A., and He, K · 2018
Later among the works it cites.
Spatially transformed adversarial examples
Xiao, C., Zhu, J.-Y., Li, B., He, W., Liu, M., and Song, D · 2018
Later among the works it cites.
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., van der Maaten, L., Yuille, A., and He, K · 2018
Later among the works it cites.
Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
Later among the works it cites.
Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Oztireli, C., and Gross, M · 2018
Later among the works it cites.
Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Athalye, A., Carlini, N., and Wagner, D · 2018
Later among the works it cites.
Random mask: Towards robust convolutional neural networks
Luo, T., Cai, T., Zhang, M., Chen, S., and Wang, L · 2018
Later among the works it cites.
Towards deep learning models resistant to adversarial attacks
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2018
Later among the works it cites.
Adversarially robust generalization requires more data
Schmidt, L., Santurkar, S., Tsipras, D., Talwar, K., and Madry, A · 2018
Later among the works it cites.
Certifiable distributional robustness with principled adversarial training
Sinha, A., Namkoong, H., and Duchi, J · 2018
Later among the works it cites.
Constructing unrestricted adversarial examples with generative models
Song, Y., Shu, R., Kushman, N., and Ermon, S · 2018
Later among the works it cites.
Robustness may be at odds with accuracy
Tsipras, D., Santurkar, S., Engstrom, L., Turner, A., and Madry, A · 2018
Later among the works it cites.
Non-local neural networks
Wang, X., Girshick, R., Gupta, A., and He, K · 2018
Later among the works it cites.
Spatially transformed adversarial examples
Xiao, C., Zhu, J.-Y., Li, B., He, W., Liu, M., and Song, D · 2018
Later among the works it cites.
Feature denoising for improving adversarial robustness
Xie, C., Wu, Y., van der Maaten, L., Yuille, A., and He, K · 2018
Later among the works it cites.
Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
Brendel, W. and Bethge, M · 2019
Closest in time.
On the sensitivity of adversarial robustness to input data distributions
Ding, G. W., Lui, K. Y.-C., Jin, X., Wang, L., and Huang, R · 2019
Closest in time.
Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
Closest in time.
Approximating cnns with bag-of-local-features models works surprisingly well on imagenet
Brendel, W. and Bethge, M · 2019
Closest in time.
On the sensitivity of adversarial robustness to input data distributions
Ding, G. W., Lui, K. Y.-C., Jin, X., Wang, L., and Huang, R · 2019
Closest in time.
Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2019
Closest in time.