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Saliency Map, the gradient of the score function with respect to the input, is the most basic technique for interpreting deep neural network decisions.
Visualizing higher-layer features of a deep network
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A. Krizhevsky and G. Hinton · 2009
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How to explain individual classification decisions
D. Baehrens, T. Schroeter, S. Harmeling, M. Kawanabe, K. Hansen, and K.-R. Müller · 2010
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
S. Bach, A. Binder, G. Montavon, F. Klauschen, K. R. Müller, and W. Samek · 2015
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Striving for simplicity: The all convolutional net
I. J. Goodfellow, J. Shlens, and C. Szegedy · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2015
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Tensorflow: A system for large-scale machine learning
M. Abadi, P. Barham, J. Chen, Z. Chen, A. Davis, J. Dean, M. Devin, S. Ghemawat, G. Irving, M. Isard, M. Kudlur, J. Levenberg, R. Monga, S. Moore, D. G. Murray, B. Steiner, P. Tucker, V. Vasudevan, P. Warden, M. Wicke, Y. Yu, and X. Zheng · 2016
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M. Sundararajan, A. Taly, and Q. Yan · 2016
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Explaining nonlinear classification decisions with deep taylor decomposition
G. Montavon, S. Lapuschkin, A. Binder, W. Samek, and K.-R. Müller · 2017
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Automatic differentiation in pytorch
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer · 2017
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Evaluating the visualization of what a deep neural network has learned
W. Samek, A. Binder, G. Montavon, S. Lapuschkin, and K.-R. Müller · 2017
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Inception-v4, inception-resnet and the impact of residual connections on learning
C. Szegedy, S. Ioffe, V. Vanhoucke, and A. A. Alemi · 2017
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Towards better understanding of gradient-based attribution methods for deep neural networks
M. Ancona, E. Ceolini, C. Öztireli, and M. Gross · 2018
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Evaluating feature importance estimates
S. Hooker, D. Erhan, P.-J. Kindermans, and B. Kim · 2018
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On the importance of single directions for generalization
A. S. Morcos, D. G. Barrett, N. C. Rabinowitz, and M. Botvinick · 2018
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A theoretical explanation for perplexing behaviors of backpropagation-based visualizations
W. Nie, Y. Zhang, and A. Patel · 2018
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Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2017
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Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
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Smoothgrad: removing noise by adding noise
D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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A. S. Ross and F. Doshi-Velez · 2018
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Bridging adversarial robustness and gradient interpretability
B. Kim, J. Seo, and T. Jeon · 2019
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Robustness may be at odds with accuracy
D. Tsipras, S. Santurkar, L. Engstrom, A. Turner, and A. Madry · 2019
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Theoretically principled trade-off between robustness and accuracy
H. Zhang, Y. Yu, J. Jiao, E. P. Xing, L. E. Ghaoui, and M. I. Jordan · 2019
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