Before revision:  Therefore, we propose a method to improve the accuracy and efficiency of inference by iteratively applying perturbations to a network that are advantageous under a Bayesian framework. The spectrum of the empirical Fisher information can be used as a measure for the difficulty of the inference during the training process.
After revision:  Therefore, we propose a method to improve the accuracy and efficiency of inference by iteratively applying perturbations to a network . Formulating active learning in the language of information geometry, our framework quantifies the difficulty of inference as well as the information gain due to perturbations through the curvature of the underlying parameter manifold as measured though the empirical Fisher information can be used as a measure for the difficulty of the inference during the training process.