Let's say I want to classify a dataset of handwritten digits (CNNs on their own can get 99.7% on the MNIST dataset but let's pretend they can only get 90% for the sake of this question).
Now, I already have some classical computer vision techniques which might be able to give me a clue. For instance, I can count the intersection points of the pen stroke
- 1,2,3,5,7 will usually have no intersection points
- 6,9 will usually have one intersection point each
- 4,8 will usually have two intersection points each (usually a 4-way crossover yields two intersection points which are close together)
So if I generate some meta-data telling me how many intersection points each sample has, how can I feed that into the CNN training so that it can take advantage of that knowledge?
My best guess is to slot it into the last fully connected layer just before classification.