  Humans are generally good at learning abstract concepts about objects and
scenes (e.g.\ spatial orientation, relative sizes, etc.). Over the last years
convolutional neural networks have achieved almost human performance in
recognizing concrete classes (i.e.\ specific object categories). This paper
tests the performance of a current CNN (GoogLeNet) on the task of
differentiating between abstract classes which are trivially differentiable for
humans. We trained and tested the CNN on the two abstract classes of horizontal
and vertical orientation and determined how well the network is able to
transfer the learned classes to other, previously unseen objects.
