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We consider the problem of explaining the decisions of deep neural networks for image recognition in terms of human-recognizable visual concepts.
On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K., and Samek, W · 1932
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The hungarian method for the assignment problem
Kuhn, H. W. and Yaw, B · 1955
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Distributed representations
Hinton, G. E., McClelland, J. L., and Rumelhart, D. E · 1986
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Introduction to Information Retrieval
Manning, C. D., Raghavan, P., and Schütze, H · 2008
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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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Structured labeling for facilitating concept evolution in machine learning
Kulesza, T., Amershi, S., Caruana, R., Fisher, D., and Charles, D · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
Simonyan, K., Vedaldi, A., and Zisserman, A · 2014
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Visualizing and understanding convolutional networks
Zeiler, M. D. and Fergus, R · 2014
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Striving for simplicity: The all convolutional net
Springenberg, J., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2015
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Generating visual explanations
Hendricks, L. A., Akata, Z., Rohrbach, M., Donahue, J., Schiele, B., and Darrell, T · 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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Setwise comparison: Consistent, scalable, continuum labels for computer vision
Sarkar, A., Morrison, C., Dorn, J. F., Bedi, R., Steinheimer, S., Boisvert, J., Burggraaff, J., D’Souza, M., Kontschieder, P., Rota Bulò, S., Walsh, L., Kamm, C. P., Zaykov, Y., Sellen, A., and Lindley, S · 2016
Cited alongside, same era.
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.
Top-down neural attention by excitation backprop
Zhang, J., Lin, Z., Brandt, J., Shen, X., and Sclaroff, S · 2016
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Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
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Learning how to explain neural networks: PatternNet and PatternAttribution
Kindermans, P.-J., Schütt, K. T., Alber, M., Müller, K.-R., Erhan, D., Kim, B., and Dähne, S · 2017
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Towards visual explanations for convolutional neural networks via input resampling
Lengerich, B. J., Konam, S., Xing, E. P., Rosenthal, S., and Veloso, M. M · 2017
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A unified approach to interpreting model predictions
Lundberg, S. M. and Lee, S.-I · 2017
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Embedding deep networks into visual explanations
Qi, Z., Khorram, S., and Li, F · 2017
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Network dissection: Quantifying interpretability of deep visual representations
Bau, D., Zhou, B., Khosla, A., Oliva, A., and Torralba, A · 2017
Cited alongside, same era.
Comparing two clusterings using matchings between clusters of clusters
Cazals, F., Mazauric, D., Tetley, R., and Watrigant, R · 2017
Cited alongside, same era.
Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y · 2017
Cited alongside, same era.
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D · 2017
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Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
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Interpretable basis decomposition for visual explanation
Zhou, B., Sun, Y., Bau, D., and Torralba, A · 2018
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