Abstract

Recent advances in learning algorithms for deep neural networks allows us to train such models efficiently to obtain rich feature representations. However, when it comes to transfer learning, local optima in the high-dimensional parameter space still pose a severe problem. Motivated by this issue, we propose a framework for performing probabilistic transfer learning in the function space while, at the same time, leveraging the rich representations offered by deep neural networks. Our approach consists of linearizing neural networks to produce a Gaussian process model with covariance function given by the network’s Jacobian matrix. The result is a closed-form probabilistic model which allows fast domain adaptation with accompanying uncertainty estimation.