Effect of Regularization Techniques on the Performance of AI Models

Abstract

Maintaining proper balance between bias and variation is a major difficulty in deep learning. When bias is too high, the model tends to underfit and fails to catch crucial patterns in the data. Conversely, excessive variance causes the model to overfit and become overly adapted to the training set, which results in subpar performance on fresh inputs. A model must achieve great accuracy on both training and test datasets while aiming for low training error and low generalization error in order to be considered effective. In this work, the CIFAR-10 picture dataset is used to construct a framework. Two methods are used to train a neural network: one without regularization and the other with regularization techniques. With a 4% gain in test accuracy and a roughly 25% decrease in test loss, the comparison demonstrates that the regularized model performs considerably better on unseen data. Practically speaking, these enhancements are noteworthy and demonstrate the model's increased dependability in real-world situations. Such a model can be successfully used in a number of image-based application fields due to its enhanced generality.