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We propose a new method for learning image attention masks in a semi-supervised setting based on the Information Bottleneck principle.
The information bottleneck method
Tishby, N., Pereira, F. C. N., and Bialek, W · 2000
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Geometric clustering using the information bottleneck method
Still, S., Bialek, W., and Bottou, L · 2003
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Image segmentation using information bottleneck method
Bardera, A., Rigau, J., Boada, I., Feixas, M., and Sbert, M · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A · 2009
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Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., and Ng, A. Y · 2011
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2014
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Deep learning and the information bottleneck principle
Tishby, N. and Zaslavsky, N · 2015
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
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Seed, expand and constrain: Three principles for weakly-supervised image segmentation
Kolesnikov, A. and Lampert, C. H · 2016
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
Cited alongside, same era.
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
Cited alongside, same era.
The deterministic information bottleneck
Strouse, D. and Schwab, D. J · 2017
Cited alongside, same era.
Object region mining with adversarial erasing: A simple classification to semantic segmentation approach
Wei, Y., Feng, J., Liang, X., Cheng, M., Zhao, Y., and Yan, S · 2017
Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Trischler, A., and Bengio, Y · 2018
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Self-erasing network for integral object attention
Hou, Q., Jiang, P., Wei, Y., and Cheng, M · 2018
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Tell me where to look: Guided attention inference network
Li, K., Wu, Z., Peng, K., Ernst, J., and Fu, Y · 2018
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Invariant representations without adversarial training
Moyer, D., Gao, S., Brekelmans, R., Galstyan, A., and Steeg, G. V · 2018
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Peng, X. B., Kanazawa, A., Toyer, S., Abbeel, P., and Levine, S · 2018
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Cited alongside, same era.
Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, 3-8 December 2018, Montréal, Canada , 2018
Bengio, S., Wallach, H. M., Larochelle, H., Grauman, K., Cesa-Bianchi, N., and Garnett, R. (eds.) · 2018
Cited alongside, same era.
The Conditional Entropy Bottleneck, 2018
Fischer, I · 2018
Cited alongside, same era.
Adversarial complementary learning for weakly supervised object localization
Zhang, X., Wei, Y., Feng, J., Yang, Y., and Huang, T. S · 2018
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The information bottleneck and geometric clustering
Strouse, D. and Schwab, D. J · 2019
Closest in time.
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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