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Deep classifiers tend to associate a few discriminative input variables with their objective function, which in turn, may hurt their generalization capabilities.
Information-bottleneck approach to salient region discovery
Zhmoginov, A., Fischer, I., and Sandler, M · 2007
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Unbiased look at dataset bias
Torralba, A. and Efros, A. A · 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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Adam: A method for stochastic optimization, 2014
Kingma, D. P. and Ba, J · 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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3d shapenets: A deep representation for volumetric shapes
Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J · 2015
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Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2016
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Real time image saliency for black box classifiers
Dabkowski, P. and Gal, Y · 2017
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Interpretable explanations of black boxes by meaningful perturbation
Fong, R. C. and Vedaldi, A · 2017
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Measuring the tendency of cnns to learn surface statistical regularities
Jo, J. and Bengio, Y · 2017
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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.
Smoothgrad: removing noise by adding noise
Smilkov, D., Thorat, N., Kim, B., Viégas, F., and Wattenberg, M · 2017
Cited alongside, same era.
Axiomatic attribution for deep networks
Sundararajan, M., Taly, A., and Yan, Q · 2017
Cited alongside, same era.
Deep convolutional networks do not classify based on global object shape
Baker, N., Lu, H., Erlikhman, G., and Kellman, P. J · 2018
Cited alongside, same era.
Interpretable convolutional neural networks
Zhang, Q., Nian Wu, Y., and Zhu, S.-C · 2018
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Unsupervised multi-task feature learning on point clouds
Hassani, K. and Haley, M · 2019
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A claim approach to understanding the pointnet
Huang, S., Zhang, B., Shen, W., and Wei, Z · 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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Dynamic graph cnn for learning on point clouds
Wang, Y., Sun, Y., Liu, Z., Sarma, S. E., Bronstein, M. M., and Solomon, J. M · 2019
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Explaining the pointnet: What has been learned inside the pointnet?
Zhang, B., Huang, S., Shen, W., and Wei, Z · 2019
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Beery, S., Van Horn, G., and Perona, P · 2018
Cited alongside, same era.
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., and Brendel, W · 2018
Cited alongside, same era.
Rosenfeld, A., Zemel, R., and Tsotsos, J. K · 2018
Cited alongside, same era.
Foldingnet: Point cloud auto-encoder via deep grid deformation
Yang, Y., Feng, C., Shen, Y., and Tian, D · 2018
Cited alongside, same era.
Pointnet: Deep learning on point sets for 3d classification and segmentation
Qi, C. R., Su, H., Mo, K., and Guibas, L. J
Cited in the paper.
Pointnet++: Deep hierarchical feature learning on point sets in a metric space
Qi, C. R., Yi, L., Su, H., and Guibas, L. J
Cited in the paper.
3d point capsule networks
Zhao, Y., Birdal, T., Deng, H., and Tombari, F · 2019
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Shortcut learning in deep neural networks
Geirhos, R., Jacobsen, J.-H., Michaelis, C., Zemel, R., Brendel, W., Bethge, M., and Wichmann, F. A · 2020
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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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