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Physical Rule-Guided Convolutional Neural Network

Kishor Datta Gupta, Marufa Kamal, Rakib Hossain Rifat, Mohd Ariful Haque, Roy George

TL;DR

A novel Physics-Guided CNN (PGCNN) architecture 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 is proposed.

Abstract

The black-box nature of Convolutional Neural Networks (CNNs) and their reliance on large datasets limit their use in complex domains with limited labeled data. Physics-Guided Neural Networks (PGNNs) have emerged to address these limitations by integrating scientific principles and real-world knowledge, enhancing model interpretability and efficiency. This paper proposes a novel Physics-Guided CNN (PGCNN) architecture 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.

Physical Rule-Guided Convolutional Neural Network

TL;DR

A novel Physics-Guided CNN (PGCNN) architecture 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 is proposed.

Abstract

The black-box nature of Convolutional Neural Networks (CNNs) and their reliance on large datasets limit their use in complex domains with limited labeled data. Physics-Guided Neural Networks (PGNNs) have emerged to address these limitations by integrating scientific principles and real-world knowledge, enhancing model interpretability and efficiency. This paper proposes a novel Physics-Guided CNN (PGCNN) architecture 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.
Paper Structure (19 sections, 4 equations, 10 figures, 6 tables, 2 algorithms)

This paper contains 19 sections, 4 equations, 10 figures, 6 tables, 2 algorithms.

Table of Contents

  1. Introduction
  2. Literature Review
  3. Methodology
  4. Shape-Based Object Detection Layer
  5. Redundancy Elimination Layer
  6. Context-Aware Weight Adjustment Layer (CAWAL)
  7. Hybrid Weight Adjustment Layer (HWAD)
  8. Experimental Result and Discussion
  9. Dataset
  10. Computational Setup and Experimental Design
  11. Results
  12. Mitigation of False Positives(FP)
  13. Optimizing Confidence Scores:
  14. Conclusion and Future Works
  15. Appendix A: Shape-Based Object Detection Layer
  16. ...and 4 more sections

Figures (10)

  • Figure 1: Physical Attributes of a Ball and Player in an Image Context
  • Figure 2: Overview of the PGCNN Framework
  • Figure 3: Loss Curve of Baseline and PGCNN Framework on DVD dataset.
  • Figure 4: Example Image of Mislabeled FP Reduction
  • Figure 5: Inference of Reduced Confidence Scores in FP
  • ...and 5 more figures