Data Augmentation in Image Classification Using Deep Learning

The chapter "Data Augmentation in Image Classification Using Deep Learning" provides a comprehensive overview of techniques used to address the challenge of limited data in deep learning. It establishes that the performance of deep learning algorithms heavily relies on large and diverse datasets, which can be costly and difficult to acquire. The chapter categorizes data augmentation methods into traditional techniques, such as geometric and color space transformations, and advanced deep learning-based strategies like Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and combination-based methods like Mixup and CutMix. The text highlights that these methods enhance model generalization, improve accuracy, and increase robustness, especially in cases of class imbalance or when working with small datasets. Ultimately, the chapter concludes that data augmentation is a fundamental and effective solution for making data-driven AI systems more sustainable, accessible, and reliable.