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Current explanation techniques towards a transparent Convolutional Neural Network (CNN) mainly focuses on building connections between the human-understandable input features with models' prediction, overlooking an alternative representation of the input, the frequency components decomposition.
On the effectiveness of low frequency perturbations
Sharma, Y., Ding, G. W., and Brubaker, M. A · 1903
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Simple black-box adversarial attacks
Guo, C., Gardner, J. R., You, Y., Wilson, A. G., and Weinberger, K. Q · 1905
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High frequency component helps explain the generalization of convolutional neural networks
Wang, H., Wu, X., Yin, P., and Xing, E. P · 1905
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On the computation of the discrete cosine transform
Narasimha, M. and Peterson, A · 1978
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Bracewell and Newbold, R · 1986
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Deep inside convolutional networks: Visualising image classification models and saliency maps, 2013
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Intriguing properties of neural networks, 2013
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Visualizing and understanding convolutional networks, 2013
Zeiler, M. D. and Fergus, R · 2013
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Explaining and harnessing adversarial examples, 2014
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Deep residual learning for image recognition, 2015
He, K., Zhang, X., Ren, S., and Sun, J · 2015
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Visualizing and understanding recurrent networks
Karpathy, A., Johnson, J., and Fei-Fei, L · 2015
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Adversarial examples in the physical world, 2016
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
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The limitations of deep learning in adversarial settings
Papernot, N., McDaniel, P., Jha, S., Fredrikson, M., Celik, Z. B., and Swami, A · 2016
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Towards evaluating the robustness of neural networks
Carlini, N. and Wagner, D · 2017
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Interpretation of neural networks is fragile, 2017
Ghorbani, A., Abid, A., and Zou, J · 2017
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Understanding black-box predictions via influence functions, 2017
Koh, P. W. and Liang, P · 2017
Low frequency adversarial perturbation, 2018
Guo, C., Frank, J. S., and Weinberger, K. Q · 2018
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Influence-directed explanations for deep convolutional networks, 2018
Leino, K., Sen, S., Datta, A., Fredrikson, M., and Li, L · 2018
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On the spectral bias of neural networks, 2018
Rahaman, N., Baratin, A., Arpit, D., Draxler, F., Lin, M., Hamprecht, F. A., Bengio, Y., and Courville, A · 2018
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Representer point selection for explaining deep neural networks, 2018
Yeh, C.-K., Kim, J. S., Yen, I. E. H., and Ravikumar, P · 2018
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Certified adversarial robustness via randomized smoothing, 2019
Cohen, J. M., Rosenfeld, E., and Kolter, J. Z · 2019
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Robustness (python library), 2019
Engstrom, L., Ilyas, A., Santurkar, S., and Tsipras, D · 2019
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Towards deep learning models resistant to adversarial attacks, 2017
Madry, A., Makelov, A., Schmidt, L., Tsipras, D., and Vladu, A · 2017
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Learning important features through propagating activation differences, 2017
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Adversarial examples are not bugs, they are features, 2019
Ilyas, A., Santurkar, S., Tsipras, D., Engstrom, L., Tran, B., and Madry, A · 2019
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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 · 2019
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Adversarial training for free!, 2019
Shafahi, A., Najibi, M., Ghiasi, A., Xu, Z., Dickerson, J., Studer, C., Davis, L. S., Taylor, G., and Goldstein, T · 2019
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