Accuracy-Preserving CNN Pruning Method under Limited Data Availability

2025-11-13

AI Summary

In data-scarce scenarios, conventional LRP-based CNN channel pruning suffers from substantial accuracy degradation and limited pruning ratios. To address this, we propose a fine-tuning-free, interpretability-driven pruning framework. Our method dynamically refines per-layer channel relevance scores by jointly leveraging structural priors from pre-trained models and an LRP-based importance re-evaluation mechanism—without requiring additional labeled data. This yields more robust channel importance estimation under low-data conditions. Experiments on ImageNet subsets and CIFAR benchmarks demonstrate that our approach achieves, on average, a 23.6% higher pruning ratio and reduces accuracy loss by 58.4% compared to state-of-the-art LRP-based pruning methods. It thus significantly overcomes the performance bottleneck of traditional LRP pruning in few-shot settings, delivering a practical, high-accuracy, high-compression solution suitable for resource-constrained edge deployment.

Technology Category

Machine Learning: Learning on the Edge & Model CompressionComputer Vision: Learning & Optimization for CVNatural Language Processing: Safety and Robustness

Application Category

Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for rankingWeb Mining and Content Analysis: Large pretrained models with web dataSecurity and Privacy: Large-scale security measurements

Abstract

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.

Problem

• Developing CNN pruning method that maintains accuracy with limited data
• Addressing accuracy degradation in existing LRP-based pruning approaches
• Achieving higher pruning rates while preserving model performance

Innovation

• LRP-based pruning without fine-tuning under data constraints
• Higher pruning rate while maintaining model accuracy
• Improved accuracy preservation compared to existing methods