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The Information Bottleneck (IB) objective uses information theory to formulate a task-performance versus robustness trade-off.
Conditional generative models are not robust
Fetaya, E., Jacobsen, J., and Zemel, R. S · 1906
Earlier work this paper cites.
Detecting out-of-distribution inputs to deep generative models using a test for typicality
Nalisnick, E., Matsukawa, A., Teh, Y. W., and Lakshminarayanan, B · 1906
Earlier work this paper cites.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift
Ovadia, Y., Fertig, E., Ren, J., Nado, Z., Sculley, D., Nowozin, S., Dillon, J. V., Lakshminarayanan, B., and Snoek, J · 1906
Earlier work this paper cites.
Guided image generation with conditional invertible neural networks
Ardizzone, L., Lüth, C., Kruse, J., Rother, C., and Köthe, U · 1907
Earlier work this paper cites.
Asymptotic methods in analysis , volume 4
De Bruijn, N. G · 1981
Earlier work this paper cites.
Large sample estimation and hypothesis testing
Newey, W. K. and McFadden, D · 1994
Earlier work this paper cites.
Note: On the interchange of derivative and expectation for likelihood ratio derivative estimators
L’Ecuyer, P · 1995
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Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, A. and Pajunen, P · 1999
Earlier work this paper cites.
The information bottleneck method
Tishby, N., Pereira, F. C. N., and Bialek, W · 2000
Earlier work this paper cites.
The im algorithm: a variational approach to information maximization
Barber, D. and Agakov, F. V · 2003
Earlier work this paper cites.
An information theoretic tradeoff between complexity and accuracy
Gilad-Bachrach, R., Navot, A., and Tishby, N · 2003
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The tradeoff between generative and discriminative classifiers
Bouchard, G. and Triggs, B · 2004
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Classification with hybrid generative/discriminative models
Raina, R., Shen, Y., Mccallum, A., and Ng, A. Y · 2004
Earlier work this paper cites.
Triangular transformations of measures
Bogachev, V. I., Kolesnikov, A. V., and Medvedev, K. V · 2005
Earlier work this paper cites.
Information bottleneck for gaussian variables
Chechik, G., Globerson, A., Tishby, N., and Weiss, Y · 2005
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Generative or discriminative? getting the best of both worlds
Bishop, C. and Lasserre, J · 2007
Earlier work this paper cites.
Pattern recognition and machine learning, 5th Edition
Bishop, C. M · 2007
Earlier work this paper cites.
Approximation theorems of mathematical statistics , volume 162
Serfling, R. J · 2009
Earlier work this paper cites.
On the generative-discriminative tradeoff approach: Interpretation, asymptotic efficiency and classification performance
Xue, J. and Titterington, D. M · 2009
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Learning and generalization with the information bottleneck
Shamir, O., Sabato, S., and Tishby, N · 2010
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Elements of information theory
Cover, T. M. and Thomas, J. A · 2012
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Multivariate information bottleneck
Friedman, N., Mosenzon, O., Slonim, N., and Tishby, N · 2013
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Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Rezende, D. J., and Welling, M · 2014
Cited alongside, same era.
MINE: mutual information neural estimation
Belghazi, I., Rajeswar, S., Baratin, A., Hjelm, R. D., and Courville, A. C · 2018
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A neural representation of sketch drawings
Ha, D. and Eck, D · 2018
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Huang, C.-W., Krueger, D., Lacoste, A., and Courville, 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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Glow: Generative flow with invertible 1x1 convolutions
Kingma, D. P. and Dhariwal, P · 2018
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Lipschitz regularity of deep neural networks: analysis and efficient estimation
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Theis, L., Oord, A. v. d., and Bethge, M · 2015
Cited alongside, same era.
Deep learning and the information bottleneck principle
Tishby, N. and Zaslavsky, N · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Rethinking the inception architecture for computer vision
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z · 2016
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Information dropout: Learning optimal representations through noisy computation
Achille, A. and Soatto, S · 2017
Cited alongside, same era.
Deep variational information bottleneck
Alemi, A. A., Fischer, I., Dillon, J. V., and Murphy, K · 2017
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Triple generative adversarial nets
Chongxuan, L., Xu, T., Zhu, J., and Zhang, B · 2017
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Virmaux, A. and Scaman, K · 2018
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Your classifier is secretly an energy based model and you should treat it like one
Grathwohl, W., Wang, K.-C., Jacobsen, J.-H., Duvenaud, D., Norouzi, M., and Swersky, K · 2019
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Excessive invariance causes adversarial vulnerability
Jacobsen, J., Behrmann, J., Zemel, R. S., and Bethge, M · 2019
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Sum-of-squares polynomial flow
Jaini, P., Selby, K. A., and Yu, Y · 2019
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Are generative classifiers more robust to adversarial attacks?
Li, Y., Bradshaw, J., and Sharma, Y · 2019
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Do deep generative models know what they don’t know?
Nalisnick, E. T., Matsukawa, A., Teh, Y. W., Görür, D., and Lakshminarayanan, B · 2019
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Hybrid models with deep and invertible features
Nalisnick, E. T., Matsukawa, A., Teh, Y. W., Görür, D., and Lakshminarayanan, B · 2019
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Towards the first adversarially robust neural network model on MNIST
Schott, L., Rauber, J., Bethge, M., and Brendel, W · 2019
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Learnability for the information bottleneck
Wu, T., Fischer, I., Chuang, I., and Tegmark, M · 2019
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Understanding and mitigating exploding inverses in invertible neural networks
Behrmann, J., Vicol, P., Wang, K.-C., Grosse, R., and Jacobsen, J.-H · 2020
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On discriminative vs. generative classifiers: A comparison of logistic regression and naive bayes
Ng, A. Y. and Jordan, M. I · 2020
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Coupling-based invertible neural networks are universal diffeomorphism approximators
Teshima, T., Ishikawa, I., Tojo, K., Oono, K., Ikeda, M., and Sugiyama, M · 2020
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A theory of usable information under computational constraints
Xu, Y., Zhao, S., Song, J., Stewart, R., and Ermon, S · 2020
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