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Despite a growing literature on explaining neural networks, no consensus has been reached on how to explain a neural network decision or how to evaluate an explanation.
Neural networks and the bias/variance dilemma
Geman, S., Bienenstock, E., and Doursat, R · 1992
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Consensus inference in neuroimaging
Hansen, L. K., Nielsen, F. Å., Strother, S. C., and Lange, N · 2001
Earlier work this paper cites.
Detection of skin cancer by classification of raman spectra
Sigurdsson, S., Philipsen, P. A., Hansen, L. K., Larsen, J., Gniadecka, M., and Wulf, H.-C · 2004
Earlier work this paper cites.
Quick shift and kernel methods for mode seeking
Vedaldi, A. and Soatto, S · 2008
Earlier work this paper cites.
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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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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Slic superpixels compared to state-of-the-art superpixel methods
Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Süsstrunk, S · 2012
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Adadelta: an adaptive learning rate method
Zeiler, M. D · 2012
Earlier work this paper cites.
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2013
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2014
Earlier work this paper cites.
Striving for simplicity: The all convolutional net
Springenberg, J., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
Earlier work this paper cites.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., and Samek, W · 2015
Earlier work this paper cites.
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
Cited alongside, same era.
Evaluating the visualization of what a deep neural network has learned
Samek, W., Binder, A., Montavon, G., Lapuschkin, S., and Müller, K.-R · 2016
Cited alongside, same era.
Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2016
Cited alongside, same era.
Xception: Deep learning with depthwise separable convolutions
Chollet, F · 2017
Cited alongside, same era.
Interpretation of Neural Networks is Fragile
Ghorbani, A., Abid, A., and Zou, J · 2017
Cited alongside, same era.
Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
Zintgraf, L. M., Cohen, T. S., Adel, T., and Welling, M · 2017
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Sanity checks for saliency maps
Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., and Kim, B · 2018
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Towards better understanding of gradient-based attribution methods for deep neural networks
Ancona, M., Ceolini, E., Oztireli, C., and Gross, M · 2018
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Explaining image classifiers by counterfactual generation
Chang, C.-H., Creager, E., Goldenberg, A., and Duvenaud, D · 2018
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The (un)reliability of saliency methods
Kindermans, P.-J., Hooker, S., Adebayo, J., Brain, G., Alber, M., Schütt, K. T., Dähne, S., Erhan, D., and Kim, B · 2017
Cited alongside, same era.
A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
Cited alongside, same era.
Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., and Müller, K.-R · 2017
Cited alongside, same era.
Grad-cam: Visual explanations from deep networks via gradient-based localization
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
Cited alongside, same era.
Learning important features through propagating activation differences
Shrikumar, A., Greenside, P., and Kundaje, A · 2017
Cited alongside, same era.
Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
Cited alongside, same era.
Interpretability beyond feature attribution: Quantitative testing with concept activation vectors (tcav)
Kim, B., Wattenberg, M., Gilmer, J., Cai, C., Wexler, J., Viegas, F., et al · 2018
Later among the works it cites.
A human-grounded evaluation benchmark for local explanations of machine learning
Mohseni, S. and Ragan, E. D · 2018
Later among the works it cites.
Structuring Neural Networks for More Explainable Predictions
Rieger, L., Chormai, P., Montavon, G., Hansen, L. K., and Müller, K.-R · 2018
Later among the works it cites.
Fairwashing: the risk of rationalization
Aïvodji, U., Arai, H., Fortineau, O., Gambs, S., Hara, S., and Tapp, A · 2019
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Explanations can be manipulated and geometry is to blame
Dombrowski, A.-K., Alber, M., Anders, C. J., Ackermann, M., Müller, K.-R., and Kessel, P · 2019
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Interpretation of neural networks is fragile
Ghorbani, A., Abid, A., and Zou, J · 2019
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Fooling neural network interpretations via adversarial model manipulation
Heo, J., Joo, S., and Moon, T · 2019
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Explainable AI: Interpreting, Explaining and Visualizing Deep Learning
Samek, W., Montavon, G., Vedaldi, A., Hansen, L. K., and Muller, K.-R · 2019
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Irof: A low resource evaluation metric for explanation methods
Anonymous · 2020
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