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.
@article{arxiv.2207.07512,
title = {Sparse Relational Reasoning with Object-Centric Representations},
author = {Alex F. Spies and Alessandra Russo and Murray Shanahan},
journal= {arXiv preprint arXiv:2207.07512},
year = {2022}
}