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This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
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Compositional boosting for computing hierarchical image structures
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The caltech-ucsd birds-200-2011 dataset
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A numerical study of the bottom-up and top-down inference processes in and-or graphs
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Object detection using strongly-supervised deformable part models
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
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Unsupervised discovery of mid-level discriminative patches
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Modeling occlusion by discriminative and-or structures
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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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Selective search for object recognition
J. R. R. Uijlings, K. E. A. van de Sande, T. Gevers, and A. W. M. Smeulders · 2013
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Detect what you can: Detecting and representing objects using holistic models and body parts
X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille · 2014
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Part detector discovery in deep convolutional neural networks
M. Simon, E. Rodner, and J. Denzler · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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How transferable are features in deep neural networks?
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2014
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Understanding deep features with computer-generated imagery
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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, A. C. Berg, and L. Fei-Fei · 2015
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Neural activation constellations: Unsupervised part model discovery with convolutional networks
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Understanding black-box predictions via influence functions
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Identifying unknown unknowns in the open world: Representations and policies for guided exploration
H. Lakkaraju, E. Kamar, R. Caruana, and E. Horvitz · 2017
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A unified approach to interpreting model predictions
S. M. Lundberg and S.-I. Lee · 2017
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Feature visualization
C. Olah, A. Mordvintsev, and L. Schubert · 2017
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Dynamic routing between capsules
S. Sabour, N. Frosst, and G. E. Hinton · 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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Object detectors emerge in deep scene cnns
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Inverting visual representations with convolutional networks
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Deep residual learning for image recognition
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One pixel attack for fooling deep neural networks
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Growing interpretable graphs on convnets via multi-shot learning
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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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Interactively transferring cnn patterns for part localization
Q. Zhang, R. Cao, S. Zhang, M. Edmonds, Y. N. Wu, and S.-C. Zhu · 2017
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Q. Zhang, Y. N. Wu, and S.-C. Zhu · 2017
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Explainable neural networks based on additive index models
J. Vaughan, A. Sudjianto, E. Brahimi, J. Chen, and V. N. Nair · 2018
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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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