We try to determine the progress made by convolutional neu- ral networks over the past 25 years in classifying images into abstract classes. For this purpose we compare the performance of LeNet to that of GoogLeNet at classifying randomly generated images which are dieren- tiated by an abstract property (e.g., one class contains two objects of the same size, the other class two objects of dierent sizes). Our results show that there is still work to do in order to solve vision problems humans are able to solve without much diculty.
['Sebastian Stabinger', 'Antonio Jose Rodríguez-Sánchez', 'Justus H. Piater']
25 years of CNNs: Can we compare to human abstraction capabilities?