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We present a simple case study, demonstrating that Variational Information Bottleneck (VIB) can improve a network's classification calibration as well as its ability to detect out-of-distribution data.
Verification of forecasts expressed in terms of probability
Brier, G. W · 1950
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013
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Adam: A method for stochastic optimization
Kingma, D. and Ba, J · 2015
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Deep Learning and the Information Bottleneck Principle
Tishby, N. and Zaslavsky, N · 2015
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Uncertainty in Deep Learning
Gal, Y · 2016
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A Baseline for Detecting Misclassified and Out-of-Distribution Examples in Neural Networks
Hendrycks, D. and Gimpel, K · 2016
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Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
Lakshminarayanan, B., Pritzel, A., and Blundell, C · 2016
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Zagoruyko, S. and Komodakis, N · 2016
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Emergence of Invariance and Disentangling in Deep Representations
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.
Shifting Mean Activation Towards Zero with Bipolar Activation Functions
Eidnes, L. and Nøkland, A · 2017
Cited alongside, same era.
On Calibration of Modern Neural Networks
Guo, C., Pleiss, G., Sun, Y., and Weinberger, K. Q · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
The Marginal Value of Adaptive Gradient Methods in Machine Learning
Wilson, A. C., Roelofs, R., Stern, M., Srebro, N., and Recht, B · 2017
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Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms, 2017
Xiao, H., Rasul, K., and Vollgraf, R · 2017
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Alemi, A. A., Poole, B., Fischer, I., Dillon, J. V., Saurous, R. A., and Murphy, K · 2018
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Learning Confidence for Out-of-Distribution Detection in Neural Networks
DeVries, T. and Taylor, G. W · 2018
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Cited alongside, same era.
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Lee, K., Lee, H., Lee, K., and Shin, J · 2017
Cited alongside, same era.
Enhancing The Reliability of Out-of-distribution Image Detection in Neural Networks
Liang, S., Li, Y., and Srikant, R · 2017
Cited alongside, same era.
Kliger, M. and Fleishman, S · 2018
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Xiao, L., Bahri, Y., Sohl-Dickstein, J., Schoenholz, S. S., and Pennington, J · 2018
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Breaking the softmax bottleneck: A high-rank RNN language model
Yang, Z., Dai, Z., Salakhutdinov, R., and Cohen, W. W · 2018
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