Visual Perceptual to Conceptual First-Order Rule Learning Networks

• URL: http://arxiv.org/abs/2604.07897v1
• Date: Thu, 09 Apr 2026 07:11:49 GMT
• Title: Visual Perceptual to Conceptual First-Order Rule Learning Networks
• Abstract summary: ILP provides a fully differentiable pipeline from image constant substitution to rule structure induction.<n>ILP achieves strong performance on classical symbolic relational datasets and on relational image data.
• Score: 16.50659243460847
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Learning rules plays a crucial role in deep learning, particularly in explainable artificial intelligence and enhancing the reasoning capabilities of large language models. While existing rule learning methods are primarily designed for symbolic data, learning rules from image data without supporting image labels and automatically inventing predicates remains a challenge. In this paper, we tackle these inductive rule learning problems from images with a framework called γILP, which provides a fully differentiable pipeline from image constant substitution to rule structure induction. Extensive experiments demonstrate that γILP achieves strong performance not only on classical symbolic relational datasets but also on relational image data and pure image datasets, such as Kandinsky patterns.

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