In this work, we investigate Batch Normalization technique and propose its probabilistic interpretation. We propose a probabilistic model and show that Batch Normalization maximizes the lower bound of its marginal log-likelihood. Then, according to the new probabilistic model, we design an algorithm which acts consistently during train and test. However, inference becomes computationally inefficient. To reduce memory and computational cost, we propose Stochastic Batch Normalization – an efficient approximation of proper inference procedure. This method provides us with a scalable uncertainty estimation technique. We demonstrate the performance of Stochastic Batch Normalization on popular architectures (including deep convolutional architectures: VGG-like and ResNets) for MNIST and CIFAR-10 datasets.
CITATION STYLE
Atanov, A., Ashukha, A., Molchanov, D., Neklyudov, K., & Vetrov, D. (2019). Uncertainty Estimation via Stochastic Batch Normalization. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11554 LNCS, pp. 261–269). Springer Verlag. https://doi.org/10.1007/978-3-030-22796-8_28
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