🤖 AI Summary
This study investigates the generalization capability and overfitting behavior of neural networks on the CIFAR-10 image classification task. By constructing and comparing a fully connected network with a convolutional architecture comprising six convolutional layers and three max-pooling layers, the work implements a complete pipeline encompassing data preprocessing (normalization and one-hot encoding), training (using the Adam optimizer with mini-batches), and validation. After ten training epochs, the model achieves a validation accuracy of 74.77% and clearly exhibits the hallmark overfitting pattern: training loss continues to decrease while validation loss begins to rise. The findings underscore the distinction between representation learning and mere memorization, offering a reproducible benchmark framework that can inform the development of regularization techniques, data augmentation strategies, and educational experimentation.
📝 Abstract
An experimental investigation of neural image classification on the CIFAR-10 benchmark is presented through fully connected and convolutional network formulations. The analysis emphasizes the complete learning pipeline: image vectorization, normalization, one-hot class encoding, supervised loss minimization, learning-rate selection, mini-batch training, convolutional feature extraction, max-pooling, and validation-based generalization assessment. A convolutional architecture with six convolutional layers and three max-pooling stages is evaluated for ten training epochs using a batch size of 128 and an Adam optimizer with a learning rate of 0.001. The validation accuracy reaches approximately 74.77%, while the validation loss begins to increase after the middle of training despite continued reduction in training loss. The resulting behavior illustrates the practical difference between representation learning and memorization, and it provides a compact experimental baseline for future studies on regularization, data augmentation, deeper architectures, and reproducible image-classification education.