A Quantitative Analysis of Basic vs. Deep Learning-based Image Data Augmentation Techniques

Abstract

Our proposed work is a research project that does quantitative analysis of various basic image manipulation techniques as processes for augmentation of image type data on the accuracy of deep learning task of hand-written digit recognition on MNIST dataset. The paper also presents a detailed comparison of various parameters such as computation burden, storage requirements for model storage, accuracy, and loss function value of the results obtained by using basic image manipulation techniques as image data augmentation techniques with those data augmentation mechanisms that are rooted in deep learning. The results that we have obtained on MNIST dataset without data augmentation applied are accuracy of 97.80% and loss of 0.320, whereas the highest accuracy was achieved by adjusting brightness as the data augmentation technique with 98.57% accuracy and 0.301 loss value. In the view of our results, we recommend that basic image manipulation-based data augmentation techniques must be used to address overfitting instead of memory or computationally expensive deep learning-based image augmentation techniques. This strategy also helps enhance the performance of various image data-based deep learning pipelines and makes these models more robust.