🤖 AI Summary
This work proposes a performance optimization approach for CIFAR-10 image classification that avoids indiscriminately increasing model complexity. Through systematic ablation studies, the authors evaluate 17 training and architectural enhancements—including learning rate scheduling, Dropout, pooling strategies, and configurations of network depth and filter counts—to identify the most effective components. These are then integrated into a weighted ensemble model to improve generalization. Emphasizing empirically driven fine-tuning over mere scaling of model size, the method achieves 89.23% accuracy on the full CIFAR-10 test set, demonstrating the efficacy of strategic component selection and ensemble learning within lightweight convolutional neural networks.
📝 Abstract
Convolutional neural networks (CNNs) remain a central approach in image classification, but their performance depends strongly on architectural and training choices. This paper presents an empirical ablation-based study of CNN optimization for the CIFAR-10 benchmark. The study evaluates 17 progressive modifications involving training duration, learning-rate scheduling, dropout configuration, pooling strategy, network depth, filter arrangement, and dense-layer design. The goal is to identify which changes improve generalization and which increase complexity without improving performance. The baseline model achieved 79.5\% test accuracy. Extending training duration improved performance steadily, whereas several structural redesigns reduced accuracy despite greater architectural variation. Based on the strongest individual configurations, a weighted ensemble was constructed, achieving 86.38\% accuracy in the reduced-data setting and 89.23\% when trained using the full CIFAR-10 dataset. These results suggest that performance gains in CNN-based classification depend less on indiscriminate increases in depth or parameter count than on careful empirical selection of training and architectural modifications. The study therefore highlights the practical value of ablation-oriented optimization and ensemble learning for small-image classification.