  There are classification tasks that take as inputs groups of images rather
than single images. In order to address such situations, we introduce a nested
multi-instance deep network. The approach is generic in that it is applicable
to general data instances, not just images. The network has several
convolutional neural networks grouped together at different stages. This
primarily differs from other previous works in that we organize instances into
relevant groups that are treated differently. We also introduce a method to
replace instances that are missing which successfully creates neutral input
instances and consistently outperforms standard fill-in methods in real world
use cases. In addition, we propose a method for manual dropout when a whole
group of instances is missing that allows us to use richer training data and
obtain higher accuracy at the end of training. With specific pretraining, we
find that the model works to great effect on our real world and pub-lic
datasets in comparison to baseline methods, justifying the different treatment
among groups of instances.
