A Comparative Analysis of L1, L2, and L1L2 Regularization Techniques in Neural Networks for Image Classification

The research examines how L1, L2, and L1L2 weight regularization methods affect neural network performance, generalization, and sparsity using the CIFAR 10 dataset. A Convolutional Neural Network (CNN) trained with the same environment for each regularization method to evaluate test accuracy, weight sparsity, and computational speed. The study shows that L1 regularization produces sparse weights, which makes models more interpretable, and L2 regularization helps prevent overfitting while improving model generalization. The combination of L1L2 regularization enables individual image classification methods to reach test accuracy. The results indicate that the weight regularization plays a vital role in creating neural networks that are both stable and efficient. They are interpretable, and L2 regularization improves generalization and reduces overfitting. The combined L1L2 regularization achieves the balance between sparsity and performance, leading to higher test accuracy compared to individual techniques for image classification. The research results demonstrate that weight regularization stands as an essential factor for Creating Neural Networks that are robust, efficient, and interpretable, thus helping to enhance Deep Learning model performance.