Evaluating the Impact of Compression Techniques on the Robustness of CNNs under Natural Corruptions

February 09, 2026

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Title: Evaluating the Impact of Compression Techniques on the Robustness of CNNs under Natural Corruptions
ArXiv ID: 2512.24971
Date: 2025-12-31
Authors: Itallo Patrick Castro Alves Da Silva, Emanuel Adler Medeiros Pereira, Erick de Andrade Barboza, Baldoino Fonseca dos Santos Neto, Marcio de Medeiros Ribeiro

📝 Abstract

Compressed deep learning models are crucial for deploying computer vision systems on resource-constrained devices. However, model compression may affect robustness, especially under natural corruption. Therefore, it is important to consider robustness evaluation while validating computer vision systems. This paper presents a comprehensive evaluation of compression techniques-quantization, pruning, and weight clustering-applied individually and in combination to convolutional neural networks (ResNet-50, VGG-19, and MobileNetV2). Using the CIFAR-10-C and CIFAR-100-C datasets, we analyze the trade-offs between robustness, accuracy, and compression ratio. Our results show that certain compression strategies not only preserve but can also improve robustness, particularly on networks with more complex architectures. Utilizing multiobjective assessment, we determine the best configurations, showing that customized technique combinations produce beneficial multiobjective results. This study provides insights into selecting compression methods for robust and efficient deployment of models in corrupted real-world environments.

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Evaluating the Impact of Compression Techniques on the Robustness of CNNs under Natural Corruptions

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