  We investigate the composability of soft-rules learned by relational neural
architectures when operating over object-centric (slot-based) representations,
under a variety of sparsity-inducing constraints. We find that increasing
sparsity, especially on features, improves the performance of some models and
leads to simpler relations. Additionally, we observe that object-centric
representations can be detrimental when not all objects are fully captured; a
failure mode to which CNNs are less prone. These findings demonstrate the
trade-offs between interpretability and performance, even for models designed
to tackle relational tasks.
