  How to aggregate information from multiple instances is a key question
multiple instance learning. Prior neural models implement different variants of
the well-known encoder-decoder strategy according to which all input features
are encoded a single, high-dimensional embedding which is then decoded to
generate an output. In this work, inspired by Choquet capacities, we propose
Capacity networks. Unlike encoder-decoders, Capacity networks generate multiple
interpretable intermediate results which can be aggregated in a semantically
meaningful space to obtain the final output. Our experiments show that
implementing this simple inductive bias leads to improvements over different
encoder-decoder architectures in a wide range of experiments. Moreover, the
interpretable intermediate results make Capacity networks interpretable by
design, which allows a semantically meaningful inspection, evaluation, and
regularization of the network internals.
