EREN
Enhancing deep learning robustness through image pre-processing
EREN (Enhancing Robustness through Efficient Normalization) is a framework that improves deep learning model robustness through intelligent image pre-processing techniques. The project focuses on developing preprocessing methods that can enhance model performance against various types of perturbations and corruptions.
Key Features
- Novel image preprocessing pipeline for enhanced robustness
- Efficient normalization techniques for real-time applications
- Comprehensive evaluation framework for robustness testing
- Integration with standard deep learning workflows
Overview of the EREN framework showing how preprocessing enhances model robustness.
Technical Implementation
The project implements several key components:
-
Preprocessing Pipeline
- Adaptive normalization techniques
- Multi-scale feature enhancement
- Real-time processing capabilities
-
Robustness Evaluation
- Comprehensive testing against common perturbations
- Performance metrics for robustness assessment
- Comparative analysis with existing methods
For more details, check out our paper (Machiraju et al., 2024).
