Accuracy-Preserving CNN Pruning Method under Limited Data Availability
This work addresses the need for efficient model compression in resource-constrained environments where data is scarce, representing an incremental improvement over prior methods.
The paper tackles the problem of significant accuracy degradation in existing LRP-based CNN pruning methods under limited data availability, proposing a new method that achieves a higher pruning rate while better preserving model accuracy.
Convolutional Neural Networks (CNNs) are widely used in image recognition and have succeeded in various domains. CNN models have become larger-scale to improve accuracy and generalization performance. Research has been conducted on compressing pre-trained models for specific target applications in environments with limited computing resources. Among model compression techniques, methods using Layer-wise Relevance Propagation (LRP), an explainable AI technique, have shown promise by achieving high pruning rates while preserving accuracy, even without fine-tuning. Because these methods do not require fine-tuning, they are suited to scenarios with limited data. However, existing LRP-based pruning approaches still suffer from significant accuracy degradation, limiting their practical usability. This study proposes a pruning method that achieves a higher pruning rate while preserving better model accuracy. Our approach to pruning with a small amount of data has achieved pruning that preserves accuracy better than existing methods.