Accuracy-Preserving CNN Pruning Method under Limited Data Availability

• URL: http://arxiv.org/abs/2511.10861v1
• Date: Thu, 13 Nov 2025 23:52:57 GMT
• Title: Accuracy-Preserving CNN Pruning Method under Limited Data Availability
• Authors: Daisuke Yasui, Toshitaka Matsuki, Hiroshi Sato,
• Abstract summary: Convolutional Neural Networks (CNNs) are widely used in image recognition and have succeeded in various domains.<n>Research has been conducted on compressing pre-trained models for specific target applications in environments with limited computing resources.<n>This study proposes a pruning method that achieves a higher pruning rate while preserving better model accuracy.
• Score: 7.647276696906605
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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