Impact Analysis of Data Augmentation on Neural Network Models

Abstract

Data augmentation is a widely employed technique in deep learning to expand the training dataset artificially[1]. Its effectiveness in enhancing neural network models’ performance across computer vision and natural language processing tasks has been widely demonstrated[2]. This thesis presents a comprehensive study on the impact of various data augmentation techniques on neural network models. Experimentswereconductedonimagedataset, evaluatingdifferentneu ral network architectures with and without dataaugmentation. Theresultsreveal significant improvements in model accuracy and robustness due to dataaugmen tation. However, the choice of augmentation technique and parameters is crucial. experiments on YOLO[3] and Faster RCNN[4] object detection models demon strate notable performance enhancements with data augmentation. These find ings underscore the efficacy of data augmentation in improving deep learning model performance, particularly for object detection tasks.