  Bayesian inference is known to provide a general framework for incorporating
prior knowledge or specific properties into machine learning models via
carefully choosing a prior distribution. In this work, we propose a new type of
prior distributions for convolutional neural networks, deep weight prior (DWP),
that exploit generative models to encourage a specific structure of trained
convolutional filters e.g., spatial correlations of weights. We define DWP in
the form of an implicit distribution and propose a method for variational
inference with such type of implicit priors. In experiments, we show that DWP
improves the performance of Bayesian neural networks when training data are
limited, and initialization of weights with samples from DWP accelerates
training of conventional convolutional neural networks.

Machine Learning;Machine Learning