  Convolutional Neural Networks (CNNs) have become the state of the art method
for image classification in the last ten years. Despite the fact that they
achieve superhuman classification accuracy on many popular datasets, they often
perform much worse on more abstract image classification tasks. We will show
that these difficult tasks are linked to relational concepts from cognitive
psychology and that despite progress over the last few years, such relational
reasoning tasks still remain difficult for current neural network
architectures.
  We will review deep learning research that is linked to relational concept
learning, even if it was not originally presented from this angle. Reviewing
the current literature, we will argue that some form of attention will be an
important component of future systems to solve relational tasks.
  In addition, we will point out the shortcomings of currently used datasets,
and we will recommend steps to make future datasets more relevant for testing
systems on relational reasoning.
