pix2rule: End-to-end Neuro-symbolic Rule Learning

• URL: http://arxiv.org/abs/2106.07487v1
• Date: Mon, 14 Jun 2021 15:19:06 GMT
• Title: pix2rule: End-to-end Neuro-symbolic Rule Learning
• Authors: Nuri Cingillioglu, Alessandra Russo
• Abstract summary: This paper presents a complete neuro-symbolic method for processing images into objects, learning relations and logical rules. The main contribution is a differentiable layer in a deep learning architecture from which symbolic relations and rules can be extracted. We demonstrate that our model scales beyond state-of-the-art symbolic learners and outperforms deep relational neural network architectures.
• Score: 84.76439511271711
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Humans have the ability to seamlessly combine low-level visual input with high-level symbolic reasoning often in the form of recognising objects, learning relations between them and applying rules. Neuro-symbolic systems aim to bring a unifying approach to connectionist and logic-based principles for visual processing and abstract reasoning respectively. This paper presents a complete neuro-symbolic method for processing images into objects, learning relations and logical rules in an end-to-end fashion. The main contribution is a differentiable layer in a deep learning architecture from which symbolic relations and rules can be extracted by pruning and thresholding. We evaluate our model using two datasets: subgraph isomorphism task for symbolic rule learning and an image classification domain with compound relations for learning objects, relations and rules. We demonstrate that our model scales beyond state-of-the-art symbolic learners and outperforms deep relational neural network architectures.

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