Physical Rule-Guided Convolutional Neural Network

kishor datta gupta, Marufa Kamal, Rakib Hossain Rifat, Mohd Ariful Haque, Roy George

Keywords: Computer Vision, Machine Learning, CNN, Rule-Guided, LLM

TL;DR: Physics-Guided CNNs enhance performance, interpretability, and confidence in data-limited scenarios.

Abstract: This paper presents a Physical-Guided Convolutional Neural Network (PGCNN) framework that incorporates dynamic, trainable, and automated LLM-generated, widely recognized rules integrated into the model as custom layers to address challenges like limited data and low confidence scores. The PGCNN is evaluated on multiple datasets, demonstrating superior performance compared to a baseline CNN model. Key improvements include a significant reduction in false positives and enhanced confidence scores for true detection. The results highlight the potential of PGCNNs to improve CNN performance for broader application areas.

Submission Number: 17