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Backpropagation-based visualizations have been proposed to interpret convolutional neural networks (CNNs), however a theory is missing to justify their behaviors: Guided backpropagation (GBP) and deconvolutional network (DeconvNet) generate more human-interpretable but less class-sensitive visualizations than saliency map.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Explaining and harnessing adversarial examples
Goodfellow, I. J., Shlens, J., and Szegedy, C · 2014
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
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Sequence to sequence learning with neural networks
Sutskever, I., Vinyals, O., and Le, Q. V · 2014
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How transferable are features in deep neural networks?
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., and Fei-Fei, L · 2016
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Classifying and segmenting microscopy images with deep multiple instance learning
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Salient deconvolutional networks
Mahendran, A. and Vedaldi, A · 2016
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Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D · 2016
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Mastering the game of go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al · 2016
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Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
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Explainable artificial intelligence (xai)
Gunning, D · 2017
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Patternnet and patternlrp–improving the interpretability of neural networks
Kindermans, P.-J., Schütt, K. T., Alber, M., Müller, K.-R., and Dähne, S · 2017
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Deconvolution and checkerboard artifacts
Odena, A., Dumoulin, V., and Olah, C · 2016
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Sub-gaussian estimators of the mean of a random vector
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Samek, W., Binder, A., Montavon, G., Lapuschkin, S., and Müller, K.-R · 2017
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Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
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