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This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., explaining knowledge representations hidden in middle conv-layers of the CNN.
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X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille · 2014
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Neural activation constellations: Unsupervised part model discovery with convolutional networks
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
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Understanding neural networks through deep visualization
J. Yosinski, J. Clune, A. Nguyen, T. Fuchs, and H. Lipson · 2015
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Object detectors emerge in deep scene cnns
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2015
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Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. G. Colmenarejo, M. W. Hoffman, D. Pfau, T. Schaul, B. Shillingford, and N. de Freitas · 2016
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
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Inverting visual representations with convolutional networks
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Harnessing deep neural networks with logic rules
Z. Hu, X. Ma, Z. Liu, E. Hovy, and E. P. Xing · 2016
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K. Li and J. Malik · 2016
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Learning deep parsimonious representations
R. Liao, A. Schwing, R. Zemel, and R. Urtasun · 2016
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Visualizing the hidden activity of artificial neural networks
P. E. Rauber, S. G. Fadel, A. X. F. ao, and A. C. Telea · 2016
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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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Growing interpretable part graphs on convnets via multi-shot learning
Q. Zhang, R. Cao, Y. N. Wu, and S.-C. Zhu · 2016
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Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
Visual explanations for convolutional neural networks via input resampling
B. J. Lengerich, S. Konam, E. P. Xing, S. Rosenthal, and M. Veloso · 2017
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Dynamic routing between capsules
S. Sabour, N. Frosst, and G. E. Hinton · 2017
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Opening the black box of deep neural networks via information
R. Schwartz-Ziv and N. Tishby · 2017
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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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Teaching compositionality to cnns
A. Stone, H. Wang, Y. Liu, D. S. Phoenix, and D. George · 2017
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Interpreting cnn models for apparent personality trait regression
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Network dissection: Quantifying interpretability of deep visual representations
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba · 2017
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Learning to learn without gradient descent by gradient descent
Y. Chen, M. W. Hoffman, S. G. Colmenarejo, M. Denil, T. P. Lillicrap, M. Botvinick, and N. de Freitas · 2017
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Towards interpretable deep neural networks by leveraging adversarial examples
Y. Dong, H. Su, J. Zhu, and F. Bao · 2017
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Streaming weak submodularity: Interpreting neural networks on the fly
E. R. Elenberg, A. G. Dimakis, M. Feldman, and A. Karbasi · 2017
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Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
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β \beta -vae: learning basic visual concepts with a constrained variational framework
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner · 2017
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C. Ventura, D. Masip, and A. Lapedriza · 2017
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Learning to reinforcement learn
J. Wang, Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Leibo, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick · 2017
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Human-explainable features for job candidate screening prediction
A. S. Wicaksana and C. C. S. Liem · 2017
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New theory cracks open the black box of deep learning
N. Wolchover · 2017
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T. Wu, X. Li, X. Song, W. Sun, L. Dong, and B. Li · 2017
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Mining object parts from cnns via active question-answering
Q. Zhang, R. Cao, Y. N. Wu, and S.-C. Zhu · 2017
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Interpreting cnn knowledge via an explanatory graph
Q. Zhang, R. Cao, F. Shi, Y. Wu, and S.-C. Zhu · 2018
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Examining cnn representations with respect to dataset bias
Q. Zhang, W. Wang, and S.-C. Zhu · 2018
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Interpretable convolutional neural networks
Q. Zhang, Y. N. Wu, and S.-C. Zhu · 2018
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Interpreting cnns via decision trees
Q. Zhang, Y. Yang, Y. N. Wu, and S.-C. Zhu · 2018
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Visual interpretability for deep learning: a survey
Q. Zhang and S.-C. Zhu · 2018
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