The Neural Networks Performance Improvement With Gaussian White Noise Augmentation

Abstract

Data augmentation is a strategy for creating synthetic data from existing data by adding slightly changed copies of current data to expand the amount of available data. When training a machine learning model, it functions as a regularizer and helps to reduce overfitting. This work will discuss the improved neural network performance with the data augmentation method employing Gaussian white noise.