  Convolutional neural networks have been achieving the best possible
accuracies in many visual pattern classification problems. However, due to the
model capacity required to capture such representations, they are often
oversensitive to overfitting and therefore require proper regularization to
generalize well. In this paper, we present a combination of regularization
techniques which work together to get better performance, we built plain CNNs,
and then we used data augmentation, dropout and customized early stopping
function, we tested and evaluated these techniques by applying models on five
famous datasets, MNIST, CIFAR10, CIFAR100, SVHN, STL10, and we achieved three
state-of-the-art-of (MNIST, SVHN, STL10) and very high-Accuracy on the other
two datasets.
