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In this work, we develop a technique to produce counterfactual visual explanations.
Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P., et al · 1998
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Labeled faces in the wild: A database for studying face recognition in unconstrained environments
Huang, G. B., Ramesh, M., Berg, T., and Learned-Miller, E · 2007
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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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The Caltech-UCSD Birds-200-2011 Dataset
Wah, C., Branson, S., Welinder, P., Perona, P., and Belongie, S · 2011
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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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Human-level concept learning through probabilistic program induction
Lake, B. M., Salakhutdinov, R., and Tenenbaum, J. B · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K. and Zisserman, A · 2015
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Striving for Simplicity: The All Convolutional Net
Springenberg, J. T., Dosovitskiy, A., Brox, T., and Riedmiller, M · 2015
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Inceptionism: Going deeper into neural networks, Jun 2015
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Machine teaching: An inverse problem to machine learning and an approach toward optimal education
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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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Top-down Neural Attention by Excitation Backprop
Zhang, J., Lin, Z., Brandt, J., Shen, X., and Sclaroff, S · 2016
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Learning deep features for discriminative localization
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., and Torralba, A · 2016
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Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
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Grad-CAM: Why did you say that? Visual Explanations from Deep Networks via Gradient-based Localization
Selvaraju, R. R., Das, A., Vedantam, R., Cogswell, M., Parikh, D., and Batra, D · 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., Goodfellow, I., Hardt, M., and Kim, B · 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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Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y · 2017
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Towards a rigorous science of interpretable machine learning
Doshi-Velez, F. and Kim, B · 2017
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Explanations based on the missing: Towards contrastive explanations with pertinent negatives
Dhurandhar, A., Chen, P.-Y., Luss, R., Tu, C.-C., Ting, P., Shanmugam, K., and Das, P · 2018
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Grounding visual explanations
Hendricks, L. A., Hu, R., Darrell, T., and Akata, Z · 2018
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Teaching categories to human learners with visual explanations
Mac Aodha, O., Su, S., Chen, Y., Perona, P., and Yue, Y · 2018
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