Bayesian convolutional neural networks : a probabilistic approach to address uncertainty in convolutional neural networks with an application in image processing

Abstract

Neural Networks (NNs) play an integral role in modern machine learning development. Recent advances in NN research have led to a wide array of applications, ranging from medical diagnosis [1] to complex problems such as facial and object recognition [2] [3]. However, despite the increasingly powerful predictive capabilities of NNs, some limitations exist which could cause more traditional methods to become the preferred alternative. Most of these limitations result from the "black box" nature of the NN in which the estimated model parameters are not interpretable. The output of traditional NNs also contain no measure of uncertainty in its predictions, causing decision-making to become challenging when NN output plays an important role such as in automatic medical imaging and autonomous vehicles. To address these challenges, we investigate a probabilistic approach to NNs through Bayesian inference and discuss di erent methods in approximating the posterior distributions of NN parameters. We investigate results when extending the NN structure to deeper architectures such as Convolutional Neural Networks and discuss the advantage of extracting additional information from the posterior predictive distribution to measure prediction uncertainty.