EXPLORING THE EFFECTIVENESS OF DATA AUGMENTATION TECHNIQUES IN MACHINE LEARNING AND DEEP LEARNING: APPLICATION TO IMAGE RECOGNITION

Dao Thi Le Thuy; Chu Ba Thanh

Dao Thi Le Thuy Faculty of Information Technology, University of Transport and Communications
Chu Ba Thanh Faculty of Information Technology, Hung Yen University of Technology and Education

Keywords: Data Augmentation, Image Recognition, Deep Learning, Generative Adversarial Networks (GANs)

Abstract

In this study, we explore data augmentation techniques aimed at enhancing the accuracy of image recognition models in machine learning and deep learning. By applying methods such as rotation, scaling, flipping, and advanced techniques like GAN-based augmentation, we address challenges related to data scarcity and imbalance. Experimental results on the Dogs vs. Cats dataset show significant improvements in model performance, with combined data augmentation techniques achieving the highest accuracy of 90.83%. This study underscores the critical role of data augmentation in improving model robustness and generalization, especially in scenarios with limited data. We also discuss the implications of these findings for future research in the field of image recognition.

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