Comprehensive analysis of convolutional neural networks applied to CIFAR-10 dataset
Biao Su · IET conference proceedings. · 2025
This article explores the advancements and applications of Convolutional Neural Networks (CNNs) in image classification, focusing on the CIFAR-10 dataset. Since their inception in 2006, CNNs have revolutionized computer vision by efficiently extracting high-level features from raw images. This study discusses the structure of CNNs, including convolutional layers, pooling, and fully connected layers, which enhance feature extraction and classification accuracy. Experimentally, CNNs have demonstrated superior performance on tasks requiring the identification of complex patterns such as in autonomous driving, security, and medical imaging. The research further investigates the optimization of CNN models through architectural enhancements, attention mechanisms, data augmentation, model compression, and cross-domain knowledge transfer. This study concludes that multicore configurations with batch processing significantly outperform single-core setups, achieving lower latency and higher throughput, thus underscoring the potent applications and continuous evolution of deep learning in modern artificial intelligence challenges.