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Mattias Teye, Hossein Azizpour, Kevin Smith. Bayesian Uncertainty Estimation for Batch Normalized Deep Networks. ICML 2018.

Teye et al. show that neural networks with batch normalization can be used to give uncertainty estimates through Monte Carlo sampling. In particular, instead of using the test mode of batch normalization, where the statistics (mean and variance) of each batch normalization layer are fixed, these statistics are computed per batch, as in training mode. To this end, for a specific query image, random batches from the training set are sampled, and prediction uncertainty is estimated using Monte Carlo sampling to compute mean and variance. This is summarized in Algorithm 1, depicting the proposed Monte Carlo Batch Normalization method. In the paper, this approach is further interpreted as approximate inference in Bayesian models.

Algorithm 1: Monte Carlo approach for using batch normalization for uncertainty estimation.

Also find this summary on ShortScience.org.

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