  We try to determine the progress made by convolutional neural networks over
the past 25 years in classifying images into abstractc lasses. For this purpose
we compare the performance of LeNet to that of GoogLeNet at classifying
randomly generated images which are differentiated by an abstract property
(e.g., one class contains two objects of the same size, the other class two
objects of different sizes). Our results show that there is still work to do in
order to solve vision problems humans are able to solve without much
difficulty.
