Hi all: I have a very fundamental question on how CNN works.
I understand fully the training process as to take a bunch of images, start with random filters, convolve, activate, calculate loss, back propagate and learn weights. Fully understood.
But once the training is done, the last convolution layer has the most complex and complete features like faces, ears, wheels and such filters that can get activated by full features.
If that is so, during testing, do we need to pass our images through all the layers again? Why don't we convolve our images against the last convolution layer and see how many of these complex feature filters get activated? And pass that on to the fully connected layer for classification?
I understand there might me inconsistencies in the layers and the inputs but except that anything more important?