Advancements in Image Data Augmentation for Improved Model Performance

: In the era of large-scale models, the demand for extensive training data in machine learning has surged, posing challenges in acquiring sufficient labeled datasets. Insufficient training data often leads to model overfitting, hindering the development of robust image classification models. Manual data labeling is costly and time-consuming, making generating diverse, high-quality labeled data a critical issue. Data augmentation emerges as a viable solution to this problem. This literature review discusses various data augmentation techniques to enhance models' generalization capabilities by expanding training samples. The review categorizes image data augmentation methods into traditional techniques, image mixing methods, and neural network-based approaches, highlighting their applications and effectiveness in improving model performance across different datasets. Ultimately, data augmentation techniques provide an efficient means of overcoming the challenges posed by limited labeled data, enabling the development of more robust and generalizable machine learning models. Future research should focus on refining these methods to further improve their applicability and effectiveness in real-world scenarios.