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Ruben Alvarez-Gonzalez¹, Andres Mendez-Vazquez¹

¹Department of Computer Science, Cinvestav Guadalajara, Zapopan 45015, Mexico.

|February 25, 2022

Summary

This study introduces a texture amortization map (TAM) to enhance feature extraction in deep convolutional neural networks (CNNs). A novel geometric classification score (GCS) measures layer learnability, optimizing CNN performance and reducing model weights.

Keywords:

CNN computer vision deep learning machine learning transfer learning

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Area of Science:

Computer Science
Artificial Intelligence
Machine Learning

Background:

Deep learning models excel at non-linear classification tasks due to advanced architectures.
Optimizing hyper-parameters and kernel selection in deep convolutional neural networks (CNNs) remains challenging for achieving high accuracy.
Deficiencies in algorithms and data processing can hinder classifier performance in practical applications.

Purpose of the Study:

To improve feature extraction in deep learning models by introducing a texture amortization map (TAM).
To develop a novel geometric classification score (GCS) for evaluating layer learnability and optimizing CNN architectures.
To reduce the complexity and computational load of deep learning models by identifying and optimizing key layers.

Main Methods:

Developed an algorithm to extract filter characteristics using a texture amortization map (TAM), considering neighboring pixel textures.
Introduced a geometric classification score (GCS) to quantify the impact of one class on another within classification problems.
Assumed data transformations in inner layers adhere to Euclidean space principles to evaluate layer contributions.

Main Results:

The texture amortization map (TAM) effectively enhances feature extraction capabilities in CNNs.
The geometric classification score (GCS) provides a quantifiable measure of layer learnability and class interaction.
The proposed methods enable the optimization of pre-trained architectures and better model generalization.

Conclusions:

The texture amortization map (TAM) and geometric classification score (GCS) offer novel approaches to improve deep convolutional neural network (CNN) performance.
Understanding feature extraction through TAM and GCS provides insights for optimizing CNN architectures and reducing model complexity.
These methods facilitate more efficient and accurate classification by refining the feature extraction process in deep learning models.