  In this paper the application of uncertainty modeling to convolutional neural
networks is evaluated. A novel method for adjusting the network's predictions
based on uncertainty information is introduced. This allows the network to be
either optimistic or pessimistic in its prediction scores. The proposed method
builds on the idea of applying dropout at test time and sampling a predictive
mean and variance from the network's output. Besides the methodological
aspects, implementation details allowing for a fast evaluation are presented.
Furthermore, a multilabel network architecture is introduced that strongly
benefits from the presented approach. In the evaluation it will be shown that
modeling uncertainty allows for improving the performance of a given model
purely at test time without any further training steps. The evaluation
considers several applications in the field of computer vision, including
object classification and detection as well as scene attribute recognition.
