We try to determine the progress made by convolutional neural 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 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.
Paper
References (10)
10Parameters used for GoogLeNet: iterations = 25000, base learning rate = 0.001, average loss = 100, weight decay = 0.002, solver = ADAM, β 1 = 0Parameters used for GoogLeNet: iterations = 25000, base learning rate = 0.001, average loss = 100, weight decay = 0.002, solver = ADAM, β 1 = 0