Advancing Image Classification Performance: A Comprehensive Study of Modern Deep Learning Architectures on CIFAR-10
We present a comprehensive analysis of modern deep learning architectures for image classification on the CIFAR-10 dataset, achieving state-of-the-art accuracy of 94.8% through an ensemble approach. Our study evaluates five distinct architectural paradigms: Enhanced ResNet (93.2%), Modified DenseNet (92.8%), Efficient-B0 variant (91.9%), Vision Transformer adaptation (90.5%), and a custom Hybrid CNN (92.4%). We introduce a novel regularization strategy combining progressive dropout, adaptive data augmentation, and dynamic weight decay, significantly improving model generalization. Through extensive ablation studies and cross-architecture analysis, we demonstrate that our ensemble method not only achieves superior accuracy but also exhibits enhanced robustness to input perturbations while maintaining computational efficiency. Our findings provide practical insights for real-world applications and contribute to the ongoing discourse on architectural design choices in deep learning.
