A Bayesian Neural Network based on Dropout Regulation
This work provides an incremental improvement in uncertainty estimation for Bayesian Neural Networks, which is relevant for applications requiring robust confidence measures.
This paper introduces "Dropout Regulation" (DR), a method that automatically adjusts the dropout rate during training using a controller. DR achieves uncertainty estimation comparable to state-of-the-art methods while maintaining implementation simplicity.
Bayesian Neural Networks (BNN) have recently emerged in the Deep Learning world for dealing with uncertainty estimation in classification tasks, and are used in many application domains such as astrophysics, autonomous driving...BNN assume a prior over the weights of a neural network instead of point estimates, enabling in this way the estimation of both aleatoric and epistemic uncertainty of the model prediction.Moreover, a particular type of BNN, namely MC Dropout, assumes a Bernoulli distribution on the weights by using Dropout.Several attempts to optimize the dropout rate exist, e.g. using a variational approach.In this paper, we present a new method called "Dropout Regulation" (DR), which consists of automatically adjusting the dropout rate during training using a controller as used in automation.DR allows for a precise estimation of the uncertainty which is comparable to the state-of-the-art while remaining simple to implement.