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This paper presents a method to explain how the information of each input variable is gradually discarded during the forward propagation in a deep neural network (DNN), which provides new perspectives to explain DNNs.
A value for n-person games
Shapley, L. S · 1953
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The information bottleneck method
Tishby, N., Pereira, F., and Bialek, W · 1999
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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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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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The caltech-ucsd birds-200-2011 dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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Imagenet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E · 2012
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Web page of the em segmentation challenge
WWW · 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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Striving for simplicity: The all convolutional net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Muller, K., and Samek, W · 2015
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NICE: non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y · 2015
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Understanding deep image representations by inverting them
Mahendran, A. and Vedaldi, A · 2015
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U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
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ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A. C., and Fei-Fei, L · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Deep learning and the information bottleneck principle
Tishby, N. and Zaslavsky, N · 2015
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Object detectors emerge in deep scene cnns
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2015
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Layer-wise relevance propagation for neural networks with local renormalization layers
Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., and Samek, W · 2016
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Inverting visual representations with convolutional networks
Dosovitskiy, A. and Brox, T · 2016
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Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding
Han, S., Mao, H., and Dally, W. J · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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“why should i trust you?” explaining the predictions of any classifier
Ribeiro, M. T., Singh, S., and Guestrin, C · 2016
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Not just a black box: Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., Shcherbina, A., and Kundaje, A · 2016
Cited alongside, same era.
Reversible architectures for arbitrarily deep residual neural networks
Chang, B., Meng, L., Haber, E., Ruthotto, L., Begert, D., and Holtham, E · 2018
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Evaluating capability of deep neural networks for image classification via information plane
Cheng, H., Lian, D., Gao, S., and Geng, Y · 2018
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Towards explanation of dnn-based prediction with guided feature inversion
Du, M., Liu, N., Song, Q., and Hu, X · 2018
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Net2vec: Quantifying and explaining how concepts are encoded by filters in deep neural networks
Fong, R. and Vedaldi, A · 2018
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i-revnet: Deep invertible networks
Jacobsen, J., Smeulders, A. W. M., and Oyallon, E · 2018
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Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2016
Cited alongside, same era.
Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
Cited alongside, same era.
Understanding black-box predictions via influence functions
Koh, P. and Liang, P · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
Svcca: Singular vector canonical correlation analysis for deep learning dynamics and interpretability
Raghu, M., Gilmer, J., Yosinski, J., and Sohl-Dickstein, J · 2017
Cited alongside, same era.
Opening the black box of deep neural networks via information
Schwartz-Ziv, R. and Tishby, N · 2017
Cited alongside, same era.
Kingma, D. P. and Dhariwal, P · 2018
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Insights on representational similarity in neural networks with canonical correlation
Morcos, A. S., Raghu, M., and Bengio, S · 2018
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Sensitivity and generalization in neural networks: An empirical study
Novak, R., Bahri, Y., Abolafia, D. A., Pennington, J., and Sohl-Dickstein, J · 2018
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Interpretable convolutional neural networks
Zhang, Q., Wu, Y. N., and Zhu, S.-C · 2018
Later among the works it cites.
Invertible residual networks
Behrmann, J., Grathwohl, W., Chen, R. T. Q., Duvenaud, D., and Jacobsen, J · 2019
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Explaining neural networks semantically and quantitatively
Chen, R., Chen, H., Huang, G., Ren, J., and Zhang, Q · 2019
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Estimating information flow in deep neural networks
Goldfeld, Z., van den Berg, E., Greenewald, K., Melnyk, I., Nguyen, N., Kingsbury, B., and Polyanskiy, Y · 2019
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Analyzing inverse problems with invertible neural networks
Gomez, A. N., Ren, M., Urtasun, R., and Grosse, R. B · 2019
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Towards a deep and unified understanding of deep neural models in nlp
Guan, C., Wang, X., Zhang, Q., Chen, R., He, D., and Xie, X · 2019
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Similarity of neural network representations revisited
Kornblith, S., Norouzi, M., Lee, H., and Hinton, G · 2019
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Infomask: Masked variational latent representation to localize chest disease
Taghanaki, S. A., Havaei, M., Berthier, T., Dutil, F., Di Jorio, L., Hamarneh, G., and Bengio, Y · 2019
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Restricting the flow: Information bottlenecks for attribution
Schulz, K., Sixt, L., Tombari, F., and Landgraf, T · 2020
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Evaluating explanation methods for deep learning in security
Warnecke, A., Arp, D., Wressnegger, C., and Rieck, K · 2020
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