  Neural networks and especially convolutional neural networks are of great
interest in current computer vision research. However, many techniques,
extensions, and modifications have been published in the past, which are not
yet used by current approaches. In this paper, we study the application of a
method called QuickProp for training of deep neural networks. In particular, we
apply QuickProp during learning and testing of fully convolutional networks for
the task of semantic segmentation. We compare QuickProp empirically with
gradient descent, which is the current standard method. Experiments suggest
that QuickProp can not compete with standard gradient descent techniques for
complex computer vision tasks like semantic segmentation.
