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Understanding Dilated Mathematical Relationship between Image Features and the Convolutional Neural Network's Learnt
Eyad Alsaghir1, Xiyu Shi1, Varuna De Silva1
1Institute for Digital Technologies, Loughborough University London, Queen Elizabeth Olympic Park, Here East, London E20 3BS, UK.
Entropy (Basel, Switzerland)
|January 21, 2022
Summary
This study reveals a mathematical link between input image features and trained convolutional neural network weights. This finding offers insights into deep learning model interpretability and efficiency.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Deep learning models require extensive data and computation for training.
- Current research lacks investigation into the mathematical relationship between input features and learned model parameters.
Purpose of the Study:
- To explore the mathematical relationship between input excitations (features) and the learned weights of a convolutional neural network.
- To investigate this relationship across training data, testing data, and the difference between them.
Main Methods:
- Training a convolutional neural network on input data.
- Extracting features from training and testing datasets.
- Analyzing the mathematical correlation between image features and model weights using ANOVA.
Main Results:
- Empirical evidence demonstrated a significant mathematical relationship between test image features and the model's learned weights.
- The study explored three specific aspects of the feature-weight relationship.
Conclusions:
- A quantifiable mathematical relationship exists between image features and convolutional neural network weights.
- This research contributes to understanding the internal workings of deep learning models and potentially improving their efficiency.
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