Optimizing Deep Neural Networks using Safety-Guided Self Compression
Mohammad Zbeeb, Mariam Salman, Mohammad Bazzi, Ammar Mohanna·May 01, 2025
Summary
A safety-driven quantization framework optimizes deep neural networks for resource-constrained devices, preserving up to 2.5% higher test accuracy with 60% size reduction. This method, using preservation sets, enhances generalization, reduces variance, and retains critical model features. It identifies key data points through Grad-CAM, uncertainty sampling, and clustering, applicable across vision and language models. Demonstrating effectiveness in reducing performance variance, promoting generalization, and minimizing overfitting, this approach selectively prunes less significant weights, suitable for resource-constrained platforms. Future research aims to extend this framework to more complex architectures and real-world applications, optimizing model performance and reliability under diverse conditions.
Introduction
Background
Overview of deep neural networks and their deployment challenges
Importance of resource-constrained devices in various applications
Objective
Aim of the safety-driven quantization framework
Goals: preserving test accuracy, reducing model size, enhancing generalization
Method
Preservation Sets
Concept and role in the framework
How preservation sets identify critical model features
Data Collection
Methods for gathering data relevant to the framework
Data Preprocessing
Techniques for preparing data for quantization
Importance of data quality in maintaining model performance
Grad-CAM, Uncertainty Sampling, and Clustering
Utilization of these methods for identifying key data points
Explanation of how these techniques contribute to the framework's effectiveness
Optimization Process
Quantization Techniques
Overview of quantization methods used in the framework
How these methods reduce model size without compromising accuracy
Performance Metrics
Metrics used to evaluate the framework's impact on test accuracy, size reduction, and generalization
Variance Reduction and Overfitting Mitigation
Strategies for minimizing variance and promoting generalization
Explanation of how the framework reduces overfitting
Application Across Domains
Vision Models
Case studies demonstrating the framework's effectiveness in vision tasks
Language Models
Illustrations of how the framework applies to language processing tasks
Generalization Across Domains
Overview of the framework's versatility and adaptability
Case Studies and Validation
Performance Analysis
Detailed results showcasing the framework's performance improvements
Real-World Applications
Examples of the framework's implementation in practical scenarios
Empirical Evidence
Data and metrics supporting the framework's claims
Future Directions
Complex Architectures
Plans for extending the framework to more sophisticated neural network designs
Real-World Applications
Strategies for scaling the framework to broader, more complex real-world problems
Enhancements and Innovations
Potential areas for future research and development
Challenges and Solutions
Discussion of anticipated challenges and proposed solutions for future implementation
Basic info
papers
machine learning
artificial intelligence
Advanced features
Insights
In what ways can this quantization framework be applied to both vision and language models?
What are the key techniques used in the framework to identify critical data points?
What are the proposed future research directions for extending the quantization framework?
How does the safety-driven quantization framework optimize deep neural networks for resource-constrained devices?