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arXiv · 2207.07512

Sparse Relational Reasoning with Object-Centric Representations

Alex F. Spies·Alessandra Russo·Murray Shanahan
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Abstract

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.

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Alex F. Spies, Alessandra Russo, Murray Shanahan. 2022-07-15. Sparse Relational Reasoning with Object-Centric Representations. https://arxiv.org/abs/2207.07512

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