Object-centric neuro-argumentative learning
File(s)
Published version
Author(s)
Type
Conference Paper
Abstract
Over the last decade, as we rely more on deep learning technologies to make critical decisions, concerns regarding their safety, reliability and interpretability have emerged. We introduce a novel Neural Argumentative Learning (NAL) architecture that integrates Assumption-Based Argumentation (ABA) with Object-Centric (OC) deep learning for im- age analysis. Our OC-NAL architecture consists of neural and symbolic components. The former segments and encodes images into facts, while the latter applies ABA learning to develop ABA frameworks enabling image classification. Experiments on synthetic data show that the OC-NAL architecture can be competitive with a state-of-the-art alternative. The code can be found at https://github.com/AbdulRJacob/Neuro-AL.
Date Issued
2025-09-08
Date Acceptance
2025-09-01
Citation
Proceedings of Machine Learning Research, 2025, 284, pp.1077-1089
Publisher
MLResearchPress
Start Page
1077
End Page
1089
Journal / Book Title
Proceedings of Machine Learning Research
Volume
284
Copyright Statement
© The authors and PMLR 2025. MLResearchPress
Source
Conference on Neurosymbolic Learning and Reasoning
Publication Status
Published
Start Date
2025-09-08
Finish Date
2025-09-10
Coverage Spatial
Santa Cruz, CA, USA