I have a binary classification problem where I have a bag of documents (image files) that I need to classify - the bag that is, not the individual document. However, a bag can have a different number of documents and the combination of documents will determine the classification. E.g.
Bag1: A, B, C label = Pass Bag2: D, E Fail Bag3: F Pass
In Bag1, the presence of A, B, or C alone may not be enough to Pass, but together they do. In Bag3, F itself might be enough to pass.
I have already done OCR to represent all the text at the Bag level. For the images, all I can think is to average the pixel values of each image in the Bag to represent it as a single image, and then train with a CNN.
Is there any better architecture or method for handling something like this?