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Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions.
Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 1910
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M. Lin, Q. Chen, and S. Yan · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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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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Why should i trust you?: Explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
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Real time image saliency for black box classifiers
P. Dabkowski and Y. Gal · 2017
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Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
Cited alongside, same era.
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
Cited alongside, same era.
Learning important features through propagating activation differences
A. Shrikumar, P. Greenside, and A. Kundaje · 2017
Cited alongside, same era.
Local explanation methods for deep neural networks lack sensitivity to parameter values
J. Adebayo, J. Gilmer, I. Goodfellow, and B. Kim · 2018
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Sanity checks for saliency maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
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Explaining image classifiers by counterfactual generation
C.-H. Chang, E. Creager, A. Goldenberg, and D. Duvenaud · 2018
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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian · 2018
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Rise: Randomized input sampling for explanation of black-box models
V. Petsiuk, A. Das, and K. Saenko · 2018
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D. Smilkov, N. Thorat, B. Kim, F. Viégas, and M. Wattenberg · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
Cited alongside, same era.
D. Omeiza, S. Speakman, C. Cintas, and K. Weldermariam · 2019
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Interpretable and fine-grained visual explanations for convolutional neural networks
J. Wagner, J. M. Kohler, T. Gindele, L. Hetzel, J. T. Wiedemer, and S. Behnke · 2019
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