  Deep learning demonstrated major abilities in solving many kinds of different
real-world problems in computer vision literature. However, they are still
strained by simple reasoning tasks that humans consider easy to solve. In this
work, we probe current state-of-the-art convolutional neural networks on a
difficult set of tasks known as the same-different problems. All the problems
require the same prerequisite to be solved correctly: understanding if two
random shapes inside the same image are the same or not. With the experiments
carried out in this work, we demonstrate that residual connections, and more
generally the skip connections, seem to have only a marginal impact on the
learning of the proposed problems. In particular, we experiment with DenseNets,
and we examine the contribution of residual and recurrent connections in
already tested architectures, ResNet-18, and CorNet-S respectively. Our
experiments show that older feed-forward networks, AlexNet and VGG, are almost
unable to learn the proposed problems, except in some specific scenarios. We
show that recently introduced architectures can converge even in the cases
where the important parts of their architecture are removed. We finally carry
out some zero-shot generalization tests, and we discover that in these
scenarios residual and recurrent connections can have a stronger impact on the
overall test accuracy. On four difficult problems from the SVRT dataset, we can
reach state-of-the-art results with respect to the previous approaches,
obtaining super-human performances on three of the four problems.
