Sparse Relational Reasoning with Object-Centric Representations

• URL: http://arxiv.org/abs/2207.07512v1
• Date: Fri, 15 Jul 2022 14:57:33 GMT
• Title: Sparse Relational Reasoning with Object-Centric Representations
• Authors: Alex F. Spies, Alessandra Russo and Murray Shanahan
• Abstract summary: We investigate the composability of soft-rules learned by relational neural architectures when operating over object-centric representations. We find that increasing sparsity, especially on features, improves the performance of some models and leads to simpler relations.
• Score: 78.83747601814669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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