Experimental Analysis of Neural Network-Based Image Classification on the CIFAR-10 Dataset
A research paper details an experimental analysis of neural network-based image classification using the CIFAR-10 dataset. The study covers the entire learning pipeline, from data preprocessing to model training and validation. A convolutional neural network achieved approximately 74.77% validation accuracy, but exhibited signs of overfitting as validation loss increased while training loss continued to decrease. AI
IMPACT Provides a baseline for future research in regularization, data augmentation, and deeper architectures for image classification tasks.